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Cross-sectional Dependence in Idiosyncratic Volatility
abstractThis paper
introduces an econometric framework for analyzing cross-sectional dependence in the idiosyncratic volatilities of assets using high frequency data.
We
first consider the estimation of standard measures of dependence in the idiosyncratic volatilities such as covariances and correlations.
Naive estimators of these measures are biased due to the use of the error-laden estimates of idiosyncratic volatilities.
We
provide bias-corrected estimators and the relevant asymptotic theory.
Next,
we introduce an idiosyncratic volatility factor model, in which we decompose the variation in idiosyncratic volatilities into two parts: the variation related to the systematic factors such as the market volatility, and the residual variation.
Again, naive
estimators of the decomposition are biased, and we provide bias-corrected estimators.
We also
provide the asymptotic theory that allows us to test whether the residual (non-systematic) components of the idiosyncratic volatilities exhibit cross-sectional dependence.
We apply
our methodology to the S&P 100 index constituents,
and document strong cross-sectional dependence in their idiosyncratic volatilities.
We consider two different sets of idiosyncratic volatility
factors, and find that neither can fully account for the cross-sectional dependence in
idiosyncratic volatilities.
For each model, we map out the
network of dependencies in residual (non-systematic) idiosyncratic
volatilities across all stocks.
Keywords:
factor model,
systematic risk,
networks of risk,
residual idiosyncratic volatility,
(co-)volatility of volatility,
high frequency data.
\\
\newline
JEL Codes: C58, C22, C14, G11.
\sloppy
\raggedbottom
Introduction
In a panel of assets, returns are generally cross-sectionally dependent.
This dependence is usually modeled using the exposure of assets to some
common return factors, such as the Fama-French factors.
In this Return Factor Model (R-FM), the total volatility of an asset return
can be decomposed into two parts: a component due to the exposure to the
common return factors (the systematic volatility), and a residual component
termed the Idiosyncratic Volatility (IdioVol). These two components of the
volatility of returns are the most popular measures of the systematic risk
and idiosyncratic risk of an asset.
Idiosyncratic Volatility is important in economics and finance for several
reasons. For example, when arbitrageurs exploit the mispricing of an
individual asset, they are exposed to the idiosyncratic risk of the asset
and not the systematic risk (see, e.g., CampbellLettauMalkielXu2001).
\footnote{
An asset is said to be mispriced with respect to a given model if the
expected value of the return on the asset is not consistent with the model.}
Also, Idiosyncratic Volatility measures the exposure to the idiosyncratic
risk in imperfectly diversified portfolios. The cross-sectional dependence
in IdioVols is also important for option pricing, see gourier2016.
The attention to IdioVols in empirical finance literature is exemplified by
two IdioVol puzzles, see CampbellLettauMalkielXu2001 and anghodrick06. A recent observation is that the IdioVols seem
to be strongly correlated in the cross-section of stocks.\footnote{
See, e.g., ConnorKorajczykLinton06, duartekamara14, herskovickellyCIV, and ChristoffersenFournierJacobs.} We propose
methods to formally study this empirical phenomenon with high-frequency
data, while fully accounting for the measurement errors in IdioVols.
This paper
provides an econometric framework for studying the cross-sectional
dependence in the Idiosyncratic Volatilities using high frequency data. The
analysis is based on a new general asymptotic theory that we develop for
estimators of quadratic covariations between nonlinear functions of spot
volatility matrices. We show that naive estimators, such as covariances and
correlations, are biased. The bias arises due to the use of error-laden
estimates of the spot volatility matrices. We provide the bias-corrected
estimators. We derive the asymptotic distribution of these estimators, and
propose consistent estimators of the asymptotic variances. We apply this new
asymptotic theory to construct tests of dependence between IdioVols and map
out the network of dependencies in IdioVols in a panel of assets.
To study Idiosyncratic Volatilities, we introduce the Idiosyncratic
Volatility Factor Model (IdioVol-FM).
Just like a Return Factor Model, R-FM, such as the Fama-French model,
decomposes returns into common and idiosyncratic returns, the IdioVol-FM
decomposes the IdioVols into systematic and residual (non-systematic)
components. The IdioVol factors may or may not be related to the return
factors. The IdioVol factors can include the volatility of the return
factors, or, more generally, (possibly non-linear) transformations of the
spot covariance matrices of any observable variables, such as the average
variance and average correlation factors of chenpetkova012. We
propose bias-corrected estimators of the components of the IdioVol-FM model.
We provide the asymptotic theory for this model. For example, it allows us
to test whether the residual (non-systematic) components of the IdioVols
exhibit cross-sectional dependence. This allows us to identify the network
of dependencies in the residual IdioVols across stocks.
Reduced-form analysis of total and idiosyncratic volatilities can be useful
to inform the formulation of structural asset pricing models. For example,
herskovickellyCIV document strong dependence in firm IdioVols, and
propose an incomplete markets asset pricing model, where IdioVol behavior is
explained by the idiosyncratic risk faced by households. When documenting the
cross-sectional dependence in IdioVol, herskovickellyCIV estimate
several volatility factor models, for example, they regress IdioVols on
average firm volatilities, where the IdioVols are defined with respect to
the market return factor or the Fama-French factors. Our framework can be
used to estimate high-frequency regressions with these variables, on a fixed
time interval, while fully capturing the effect of the measurement error
from the preliminary estimation of both the dependent variable and the
factor.
Throughout the paper, we use factors that are specified by the researcher.
An example of our Return Factor Model is the so-called Fama-French factor
model, which has three observable factors, or the CAPM, which has one
observable factor (the market portfolio return). An example of our IdioVol
factors is the market volatility, which can be estimated from the market
index. Thus, our setup is different from settings such as PCA where factors
are identified from the cross-section of the assets studied. The treatment
of the latter case adds an additional layer of complexity to the model and
is beyond the scope of the current paper.
We apply our methodology
to high-frequency data on the S&P 100 index constituents. We study the
IdioVols with respect to two models for asset returns: the CAPM and the
three-factor Fama-French model.\footnote{
The high frequency Fama-French factors are provided by yackalninaxiu-FF.} In both cases, the average pairwise correlation between
the IdioVols is high (0.35). We verify that this dependence cannot be
explained by the missing return factors. This confirms the recent findings
of herskovickellyCIV who use low frequency (daily and monthly) return
data.
We then consider the IdioVol-FM. We use two sets of IdioVol factors: the
market volatility alone and the market volatility together with volatilities
of nine industry ETFs. With the market volatility as the only IdioVol
factor, the average pairwise correlation between residual (non-systematic)
IdioVols is substantially lower (0.21)
than between the total IdioVols. With the additional industry ETF
volatilities as IdioVol factors, average correlation between the residual
IdioVols decreases further (to 0.17). However, neither of the two sets of
the IdioVol factors can fully explain the cross-sectional dependence in the
IdioVols. For each model, we map out the network of dependencies in residual
IdioVols across all stocks.
This paper analyzes cross-sectional dependence in Idiosyncratic
Volatilities. This should not be confused with the analysis of
cross-sectional dependence in total and idiosyncratic returns. A
growing number of papers study the latter question using high frequency
data.
These date back to the analysis of realized covariances and their
transformations, see, e.g., barndorffnielsenshephard04 and ABDW2006. A continuous-time factor model for asset returns with observable
return factors was first studied in myklandzhang2006. Various return
factor models with observable factors have been studied by, among others,
BollerslevTodorovJOE2010, FanFurgerXiu16, litodorovtauchen17-adaptive, litodorovtauchen-jumpreg, and yackalninaxiu-FF. Emerging literature also studies the cross-sectional
dependence in returns using high-frequency data and latent return factors,
see
AitXiuPCA,AitXiuPCA_HighDim and Pelger2019_theory,Pelger2019_applied.
Importantly, the models in the above papers are silent on the
cross-sectional dependence structure in the IdioVols.
While this paper focuses on the study of cross-sectional dependence of
IdioVols, our new asymptotic theory can be used in various other
applications. For example, we can estimate dependence measures, in the form
of co-volatilities or the corresponding correlations, between the
time-varying asset betas.\footnote{
Here, asset betas are the loadings of asset returns on return factors; these
are distinct from the asset volatility betas that we describe in the next
section.} While it is well-known that asset betas vary over time in
practice, there is no consensus as to what common factors drive this
variation, so accurate dependence measures of asset beta co-movement can be
helpful. Another example is the estimation of dependence measures between
total volatilities or systematic volatilities of asset returns. In addition,
we can estimate high-frequency regressions of one element of a spot
volatility matrix on other elements, such as regression of the asset
volatility on market volatility. Finally, we can estimate high-frequency
regressions of total asset volatility on average asset volatility, which
mirrors one more of the specifications considered in herskovickellyCIV
, in addition to the specifications described earlier.
Our inference theory is related to
several estimators in the existing literature. The closest are the
volatility of volatility estimator of vetter-vovo and one of the
asymptotic bias estimators of jacodrosenbaum-sqrtn. vetter-vovo proposes an estimator of
volatility of volatility of the returns of one asset, and derives the
relevant theory for inference.\footnote{
This estimator is also studied in yacjacod14 (Section 8.3) under
similar assumptions to vetter-vovo. yacjacod14 cite 2011
working paper version of vetter-vovo.}
We extend the analysis to the multivariate case with nonlinear
transformations, return jumps, and volatility jumps. While jacodrosenbaum-sqrtn focus on a different problem, one of the asymptotic
bias terms in their paper coincides with our quantity of interest in a
special case, see Section (ref) for
details. The setting in jacodrosenbaum-sqrtn is multivariate and
robust to return and volatility jumps, but they only establish consistency
of the relevant estimator, and do not provide any asymptotic distribution
theory. In contrast, we derive the asymptotic distribution, as well as the
consistency of the estimator of the asymptotic variance. See also LiLiuZhang2012-VoV who extend the results in vetter-vovo to allow
for price jumps and market microstructure noise. They do not consider the
multivariate case, nonlinear transformations, or volatility jumps.
Finally,
ChongTodorov2024JOE propose nonparametric estimators of the
volatility of volatility and leverage effect using high-frequency data on short-dated options.
jacodrosenbaum13,jacodrosenbaum-sqrtn, litodorovtauchen-dependencies and LiLiuXiu2019-jackknife estimate
integrated functionals of volatilities, which includes Idiosyncratic
Volatilities. The latter problem is simpler than the problem of the current
paper in the sense that $\sqrt{n}$-consistent estimation is possible, and
the estimators are consistent without a bias correction (see Section (ref) for details). In the literature on the
estimation of the leverage effect, preliminary estimation of volatility also
creates a bias, which also needs to be corrected to achieve consistency, see
aitfanli13, Yacine-jump-lev, kalninaxiu-lev and WangMykland12.
One of the reasons why we can account for the measurement error from
preliminary estimation of volatilities is the fact that our framework only
uses one (in-fill) asymptotic approximation. It is interesting to contrast this approach with the
analysis of two-step estimators using joint in-fill and long-span
asymptotics, see, e.g., corrdidistaso06, Todorov2009, bandi-reno-2012, kanaya-kristensen-2016, and LiPatton2018.
In these double asymptotic settings, the inference methods for the second
step typically do not depend on the first-step measurement error. This
provides a good approximation as long as the number of high-frequency
observations in every low-frequency period is large enough. A notable early
exception is BollerslevZhou2002 who use a simple parametric model for
the first-step measurement error.
The Realized Beta GARCH model of hansen-lunde-voev-2014 imposes a
structure on the cross-sectional dependence in IdioVols. This structure is
tightly linked with the Return Factor Model parameters, whereas our
stochastic volatility framework allows separate specification of the return
factors and the IdioVol factors.\footnote{
In the Beta GARCH model, the IdioVol of a stock is a product of its own
(total) volatility, and one minus the square of the correlation between the
stock return and the market return.}
In the empirical section, we define a network of dependencies using
(functions of) quadratic covariations of IdioVols. This approach can be
compared with the network connectedness measures of dieboldyilmaz2014
.
The latter measures are based on forecast error variance decompositions from
vector autoregressions. They capture co-movements in forecast errors. In
contrast, we assume a general semimartingale setting, and our framework
captures realized co-movements in Idiosyncratic Volatilities, while
accounting for the measurement errors in these volatilities.
The remainder of the paper is organized as follows. Section (ref) introduces the model and the quantities of interest. Section (ref) describes the identification and estimation. Section (ref) presents the asymptotic properties of our estimators.
Section (ref) uses high-frequency stock return data to study
the cross-sectional dependence in IdioVols using our framework. Section (ref) contains Monte Carlo simulations. The Online Supplementary Appendix
contains all proofs and additional figures.
Model and Quantities of Interest
We first describe a general Factor Model for the Returns
(R-FM), which allows us to define the Idiosyncratic Volatility. We then
introduce the Idiosyncratic Volatility Factor Model (IdioVol-FM). In this
framework, we proceed to define the cross-sectional measures of dependence
between the total IdioVols, as well as the residual IdioVols, which take
into account the dependence induced by the IdioVol factors.
Suppose we have (log) prices on $d_{S}$ assets such as stocks, $
S_{t}=(S_{1,t},\ldots ,S_{d_{S},t})^{\top }$, and on $d_{F}$ observable
factors, $F_{t}=(F_{1,t},\ldots ,F_{d_{F},t})^{\top }$. We stack them into
the $d$-dimensional process $Y_{t}=(S_{1,t},\ldots
,S_{d_{S},t},F_{1,t},\ldots ,F_{d_{F},t})^{\top }$ where $d=d_{S}+d_{F}$.
The observable factors $F_{1},\ldots, F_{d_{F}}$ are used in the R-FM model
below. We assume that all observable variables jointly follow an It\^{o}
semimartingale, i.e., $Y_{t}$ follows
equation[equation omitted — 117 chars of source]
where $W$ is a $d^{W}$-dimensional Brownian motion ($d^{W}\geq d$),
$C_{t}={\Greekmath 011B} _{t}{\Greekmath 011B} _{t}^{\top }$ is the spot covariance process, and $
J_{t}^{Y}$ denotes a finite variation jump process. The spot covariance
matrix process $C_{t}$
of $Y_{t}$ is a continuous It\^{o} semimartingale,\footnote{
Note that assuming that $Y$ and $C$ are driven by the same $d^{W}$
-dimensional Brownian motion $W$ is without loss of generality provided that
$d^{W}$ is large enough, see, e.g., equation (8.12) of yacjacod14.}
equation[equation omitted — 160 chars of source]
We refer to the $\left( C_{t}\right) _{a,b}$ element of the matrix $C_{t}$
as $C_{ab,t}$. For convenience, we also use the alternative notation $
C_{UV,t}$ to refer to the spot covariance between two elements $U$ and $V$
of $Y$, and $C_{U,t}$ to refer to $C_{UU,t}$.
We assume a standard continuous-time factor model for the asset returns.
\begin{def1}
For all $0\leq t\leq T$ and $j=1,\ldots ,d_{S}$,
\footnote{\abovedisplayskip=1pt \belowdisplayskip=3pt Quadratic covariation
of two vector-valued It\^{o} semimartingales $X$ and $Y $, over the time
span $[0,T]$, is defined as
equation*[equation* omitted — 174 chars of source]
for any $t_{0}<t_{1}<\ldots <t_{M}=T$ with $\sup_{s}\left\vert
t_{s+1}-t_{s}\right\vert \rightarrow 0$ as $M\rightarrow \infty $.
Intuitively, quadratic covariation can be thought of as
the integrated covariance between the increments $dX_{t}$ and $dY_{t}$.}
equation[equation omitted — 276 chars of source]
\end{def1}
In the above, $dZ_{j,t}$ is the idiosyncratic return of stock $j$. The
superscripts $c$ and $d$ indicate the continuous and jump part of the
processes, so that ${\Greekmath 010C}_{j,t}$ and $\tilde{{\Greekmath 010C}}_{j,t}$ are the
continuous and jump factor loadings. For example, the $k$-th component of $
{\Greekmath 010C}_{j,t}$ corresponds to the time-varying loading of the continuous part
of the return on stock $j$ to the continuous part of the return on the $k$
-th factor. We set ${\Greekmath 010C}_t=({\Greekmath 010C}_{1,t},\ldots,{\Greekmath 010C}_{d_S,t})^\top$ and $
Z_t=(Z_{1,t},\ldots,Z_{d_S,t})^\top$.
We do not need the return factors $F_t$ to be the same across assets to
identify the model, but without loss of generality, we keep this structure
as it is standard in empirical finance. These return factors are assumed to
be observable, which is also standard. For example, in the empirical
application, we use two sets of return factors: the market portfolio and the
three Fama-French factors, which are constructed in yackalninaxiu-FF.
A continuous-time factor model for returns with observable factors was
originally studied in myklandzhang2006 in the case of one factor and
in the absence of jumps. A burgeoning literature uses related models to
study the cross-sectional dependence of total and/or idiosyncratic returns.
However, this literature does not consider the cross-sectional dependence in
the IdioVols.
We define the idiosyncratic Volatility (IdioVol) to be the spot volatility
of $Z_{j,t}$ and denote it by $C_{Zj,t}$.
Notice that R-FM in ((ref)) implies that the factor loadings ${\Greekmath 010C}
_{t}$ as well as the IdioVols are functions of the total spot covariance
matrix $C_{t}$. In particular, the vector of factor loadings satisfies
equation[equation omitted — 85 chars of source]
for $j=1,\ldots ,d_{S}$, where $C_{F,t}$
denotes the spot covariance matrix of the factors $F$, which is the lower $
d_{F}\times d_{F}$ sub-matrix of $C_{t}$; and $C_{FSj,t}$ denotes the
covariance of the factors and the $j^{th}$ stock, which is a vector
consisting of the last $d_{F}$ elements of the $j^{th}$ column of $C_{t}$.
The IdioVol of stock $j$ is then also a function of the total spot
covariance matrix $C_{t}$,
equation[equation omitted — 336 chars of source]
By the It\^{o} lemma, ((ref)) and ((ref)) imply
that factor loadings and IdioVols are also It\^{o} semimartingales with
characteristics that are functions of $C_{t}$.
We now introduce the Idiosyncratic Volatility Factor model (IdioVol-FM). In
IdioVol-FM, the cross-sectional dependence in the IdioVol shocks can be
potentially explained by certain IdioVol factors we denote as $\Pi _{t}$. A
simple example of IdioVol factor is the market volatility. Our model allows
IdioVol factors to be any given smooth functions of the matrix $C_{t}$; we
discuss examples below.
\begin{def2}
For all $0\leq t\leq T$ and $j=1,\ldots ,d_{S}$, the Idiosyncratic
Volatility $C_{Zj}$ follows,
eqnarray[eqnarray omitted — 302 chars of source]
where $\Pi _{t}=(\Pi _{1t},\ldots ,\Pi _{d_{\Pi }t})$ is a $\mathbb{R}
^{d_{\Pi }}$-valued vector of IdioVol factors. IdioVol factors satisfy
equation[equation omitted — 57 chars of source]
with the function $\Pi _{k}(\cdot )$ being three times continuously
differentiable for $k=1,\ldots ,d_{\Pi }$.
\end{def2}
$\Pi \left( \cdot \right) $ is a smooth function of $C_{t}$. For example,
often $\Pi (C_{t})$ is $C_{F,t}$, i.e., $\Pi \left( \cdot \right) $ selects
the components of $C_{t}$ that correspond to the volatilities of the
observable factors $F_{t}$.
More generally, $\Pi_{t} $ may also include the volatilities and covolatilities of other assets beyond $F_t$.
Even more generally, our theory permits a rather wide class of IdioVol factors,
since it includes general non-linear transforms of the spot covariance matrix process $C_{t}$.
For example, IdioVol factors can be
linear combinations of the total volatilities of assets, see, e.g., the
average variance factor of chenpetkova012. Another example is the
common IdioVol factor, or \textquotedblleft CIV\textquotedblright , which is
studied in herskovickellyCIV. CIV is defined as the cross-sectional
average of the firm IdioVols from CAPM.
The IdioVol factors can also be
the volatilities of any other observable processes.
We call the residual term $C_{Zj,t}^{resid}$ in the IdioVol-FM the residual
IdioVol of asset $j$.
Our assumptions imply that the components of the IdioVol-FM, $C_{Zj,t},\Pi
_{t}$ and $C_{Zj,t}^{resid}$, are It\^{o}
semimartingales. We remark that both the dependent variable and the
regressors in our IdioVol-FM are not directly observable and have to be
estimated, and our asymptotic theory takes that into account. As will see in
Section (ref), this preliminary estimation implies that the
naive estimators of all the dependence measures defined below are biased.
One of the contributions of this paper is to quantify this bias and provide
the bias-corrected estimators for all the quantities of interest.
Having specified our econometric framework, we now provide the definitions
of some natural measures of dependence of (the continuous parts of) the
(total) IdioVols and the residual IdioVols. We consider the estimation of these
measures in Section (ref).
Before studying the decomposition of the IdioVol-FM model,
one may be interested in quantifying the dependence between
the (total) IdioVols of two stocks $j$ and $s$.
Quadratic covariation $
[C_{Zj},C_{Zs}]_{T}^{c}$ is\ one natural measure of dependence between the
(continuous parts of) the IdioVols $C_{Zj}$ and $C_{Zs}$. Another natural
and scale invariant measure is the quadratic-covariation-based correlation
between the two IdioVol processes over a given time period $[0,T]$,
equation[equation omitted — 164 chars of source]
Correlation-based measure is more convenient for reporting the strength of
dependence, while the quadratic covariation $[C_{Zj},C_{Zs}]_{T}^{c}$
without normalization is more convenient for testing for the presence of
cross-sectional dependence in IdioVols. We consider such tests in Section
(ref).
Similarly, to measure the cross-sectional dependence between the residual
IdioVols of two stocks, after accounting for the effect of the IdioVol
factors, we use the quadratic-covariation-based correlation,
equation[equation omitted — 232 chars of source]
In Section (ref), we use the quadratic covariation between the
two residual IdioVol processes $[C_{Zj}^{resid},C_{Zs}^{resid}]_{T}^{c}$
without normalization for testing purposes.
We want to capture how well the IdioVol factors explain the time variation
of IdioVols of the $j^{th}$ asset. For this purpose, we use the
quadratic-covariation based analog of the coefficient of determination. For $
j=1,\ldots ,d_{S}$,
equation[equation omitted — 176 chars of source]
It is interesting to compare the correlation measure between IdioVols in
equation ((ref)) with the correlation between the residual parts
of IdioVols in ((ref)). We consider their difference,
equation[equation omitted — 96 chars of source]
to see how much of the dependence between IdioVols can be attributed to the
IdioVol factors. In practice, if we compare assets that are known to have
positive covolatilities (typically, stocks have that property), another
useful measure of the common part in the overall covariation between
IdioVols is the following quantity,
equation[equation omitted — 176 chars of source]
This measure is bounded by 1 if the covariations between residual IdioVols
are nonnegative and smaller than the covariations between IdioVols, which is
what we find for every pair in our empirical application with high-frequency
observations on stock returns.
We remark that our framework can be compared with the following null
hypothesis studied in litodorovtauchen-dependencies, $
H_{0}:~C_{Zj,t}=a_{Zj}+{\Greekmath 010D} _{Zj}^{\top }\Pi _{t},~0\leq t\leq T$. This $
H_{0}$ implies that the IdioVol is a deterministic function of the factors,
which does not allow for an error term. In particular, this null hypothesis
implies $R_{Zj}^{2,\textit{IdioVol-FM}}=1$. Our framework allows for testing
stochastic relationships, i.e., null hypotheses $H_{0}:{\Greekmath 010D} _{Zj}^{\top
}=0 $ in the presence of an error term.
Estimation
As we show below, the quantities of interest in Section (ref) can
be expressed in terms of the continuous quadratic covariation between two
functions of the spot covariance matrix $C_{t}$,
equation[equation omitted — 68 chars of source]
Section (ref) proposes estimators of this
general functional, and Section (ref) explains
how to use these formulas to obtain estimators of the quantities of interest
in Section (ref).
Estimation of a General Functional
This section proposes estimators of the continuous quadratic covariation
between two functions of the spot covariance matrix $[H(C),G(C)]_{T}^{c}$,
where $H$ and $G$ are given real-valued smooth functions. Recall that $C_{t}$
is the spot covariance matrix of the observable variables, see equations (
(ref))-((ref)).
Suppose we have discrete observations on $Y_{t}$ over an interval $[0,T]$.
Denote by $\Delta _{n}$ the distance between observations. It is well known
that we can estimate the spot covariance matrix $C_{t}$ at time $(i-1)\Delta
_{n}$ with a local truncated realized volatility estimator,
equation[equation omitted — 236 chars of source]
where $\Delta _{i}^{n}Y=Y_{i\Delta _{n}}-Y_{(i-1)\Delta _{n}}$ and where $
k_{n}$ is the number of observations in a local window.\footnote{
It is also possible to define more flexible kernel-based estimators as in
kristensen10.} We refer to the $\left( \widehat{C}_{i\Delta
_{n}}\right) _{a,b}$ element of the matrix $\widehat{C}_{i\Delta _{n}}$ as $
\widehat{C}_{ab,i\Delta _{n}}$.
If $C_{i\Delta _{n}}$ was observed and in the absence of volatility jumps,
we could estimate $[H(C),G(C)]_{T}$ by the realized covariance between $
G(C_{i\Delta _{n}})$ and $H(C_{i\Delta _{n}})$, which is the sample analog
of the definition of $[H(C),G(C)]_{T}$. However, we do not observe $
C_{i\Delta _{n}}$. If we replace it with $\widehat{C}_{i\Delta _{n}}$ in (
(ref)), we obtain the plug-in estimator
equation[equation omitted — 306 chars of source]
However, it turns out that due to the measurement errors in $\widehat{C}
_{i\Delta _{n}}$, this estimator is inconsistent.
We propose two estimators for the general quantity $[H(C),G(C)]_{T}^{c}$.
Our first estimator is a bias-corrected sample analog of the definition of
quadratic covariation between two It\^{o} processes,
align[align omitted — 604 chars of source]
where the indicator function should only be applied if we are concerned
about volatility jumps, and thus we want to truncate them.\ In the above, we
denote by $A_{i}$ the event of not detecting a volatility jump in the
interval $\left( i\Delta _{n},\left( i+k_{n}\right) \Delta _{n}\right] $,
defined as $A_{i}\equiv \{||\widehat{C}_{\left( i+k_{n}\right) \Delta _{n}}-
\widehat{C}_{\left( i-k_{n}\right) \Delta _{n}}||<u_{n}^{\prime }\}$, where $
u_{n}^{\prime }$ is some threshold.
Our second estimator is based on the following equality, which follows by
the It\^{o} lemma,
equation[equation omitted — 178 chars of source]
where $\overline{C}_{t}^{gh,ab}$ denotes the continuous covariation between
the volatility processes $C_{gh,t}$ and $C_{ab,t}$. The quantity is thus a
non-linear functional of the spot covariance and spot volatility of
volatility matrices. Our second estimator is a bias-corrected version of the
sample counterpart of the \textquotedblleft linearized\textquotedblright\
expression in ((ref)),\footnote{
The computation time for any of our two estimators is increasing with the
number of stocks and factors $d$. In practice, we compute all the quantities
of interest for pairs of stocks, so $d_{S}=2$ and thus $d=d_{F}+2$.}
eqnarray[eqnarray omitted — 600 chars of source]
We now provide the intuition for the bias terms. Suppose volatility is
continuous. If we had observations on $C_{i\Delta _{n}}$, the estimators of $
[H(C),G(C)]_{T}$ would not need any bias-correction terms. It is useful to
think of $\widehat{C}_{i\Delta _{n}}$ as an estimator of integrated
volatility matrix, $\widehat{C}_{i\Delta _{n}}=\frac{1}{k_{n}\Delta _{n}}
\int_{i\Delta _{n}}^{\left( i+k_{n}\right) \Delta _{n}}C_{s}ds+U_{i\Delta
_{n}}$, where $U_{i\Delta _{n}}$ is the estimation error.
The first part of the bias-correction in ((ref)) and ((ref)) is an additive term
equation[equation omitted — 333 chars of source]
This term arises because of the estimation error $U_{i\Delta _{n}}$.
Intuitively, estimation of, e.g., variance of functionals of $C_{i\Delta
_{n}}$ by variance of functionals of $\widehat{C}_{i\Delta _{n}}$
overestimates it due to the additional variability of $U_{i\Delta _{n}}$. In
particular, one can show that the additive bias-correction term in ((ref)) is, up to a scale factor, an estimator of the
asymptotic covariance between the estimators of $\int_{0}^{T}H(C_{t})dt$ and
$\int_{0}^{T}G(C_{t})dt$.
The second part of the bias-correction in ((ref)) and ((ref)) is the multiplicative correction factor $3/2$. This correction
factor is needed because of a smoothing bias that arises due to the
replacement of $C_{i\Delta _{n}}$ by $\frac{1}{\Delta _{n}}\int_{i\Delta
_{n}}^{\left( i+k_{n}\right) \Delta _{n}}C_{s}ds$. To gain some intuition,
consider the special case of $d=1$ and $H\left( \cdot \right) =G\left( \cdot
\right) =\cdot \ $.
Suppose we had observations on $\frac{1}{\Delta _{n}}\int_{i\Delta
_{n}}^{\left( i+k_{n}\right) \Delta _{n}}C_{s}ds$. The $i^{th}$ summand in
the naive estimator of
$\left[ C,C\right] _{T}$ would be
equation[equation omitted — 339 chars of source]
divided by $\Delta _{n}^{2}k_{n}^{3}$. Consider the weights that the
integral $\int_{i\Delta _{n}}^{\left( i+k_{n}\right) \Delta _{n}}\left(
C_{s+\Delta _{n}k_{n}}-C_{s}\right) ds$ puts on $\Delta _{n}$-
increments
of the volatility $C_{t}$:
these weights are triangular, i.e.,
$\left( \Delta _{n}k_{n}-\left\vert \Delta _{n}k_{n}+i\Delta
_{n}-s\right\vert \right) I\left\{ s\in \left[ i\Delta _{n},\left(
i+2k_{n}\right) \Delta _{n}\right] \right\} $.
One can show that the squared integral in ((ref)
) is proportional to the integral of the squared triangular weights, $\frac{1
}{\left( \Delta _{n}k_{n}\right) ^{3}}\int_{i\Delta _{n}}^{\left(
2k_{n}+i\right) \Delta _{n}}\left( \Delta _{n}k_{n}-\left\vert \Delta
_{n}k_{n}+i\Delta _{n}-s\right\vert \right) ^{2}ds$. The latter integral
equals $\frac{2}{3}$, hence the estimator needs a multiplicative correction
factor $\frac{3}{2}$.
When $H(\cdot )=G(\cdot )$, the estimand is nonnegative, $\left[ H(C),G(C)
\right] _{T}^{c}\geq 0$, so our estimators are nonnegative in large samples.
However, due to the presence of an additive bias-correction, our estimators
are not guaranteed to be nonnegative in finite samples. We remark that vetter-vovo constructs a univariate volatility of volatility estimator
that is guaranteed to be nonnegative, at the cost of a slower rate of
convergence.
Our two estimators, AN in equation ((ref)) and LIN in ((ref)), are identical when $H$ and $G$ are linear, for example, when
estimating the covariation between two volatility processes. In the
univariate case $d=1$, when $H(\cdot )=G(\cdot )=\cdot $\ , and when one
assumes no price or volatility jumps and omits the price and volatility jump
truncation, both of our estimators coincide with the volatility of
volatility estimator of vetter-vovo.
While jacodrosenbaum-sqrtn focus on a different problem, one of the
asymptotic bias terms in their paper is of the form $\left[ H(C),H(C)\right]
_{T}^{c}$. In the special case $H(\cdot )=G(\cdot )$, aside from a scale
factor, the end-effects, and the form of the volatility jump truncation, our
LIN estimator in equation ((ref)) coincides with their estimator.
Our approach to volatility jumps differs as we truncate these jump from
below, while jacodrosenbaum-sqrtn truncate from above, and we use a
simpler form of truncation that in finite samples is robust to consecutive
volatility jumps. jacodrosenbaum-sqrtn only establish consistency of
the relevant estimator, and do not provide any asymptotic distribution
theory. In contrast, we derive the asymptotic distribution of the estimators
of $\left[ H(C),G(C)\right] _{T}^{c}$, and provide a consistent estimator of
the asymptotic variance.
Estimation in R-FM and IdioVol-FM models
In this section, we explain how to use the formulas in equations ((ref)
) and ((ref)) to obtain estimators for the objects of interest in
Section (ref), see equations ((ref))--((ref)). In particular, each of these objects of interest,
equation[equation omitted — 498 chars of source]
for $j,s=1,\ldots ,d_{S}$, can be written as
equation[equation omitted — 204 chars of source]
for some smooth, real-valued functions ${\Greekmath 0127} $, $H_{r}$, $G_{r}$, $
r=1,\ldots ,{\Greekmath 0114} $. Each element in ((ref)) is of
the form $[H_{r}(C),G_{r}(C)]_{T}^{c}$, i.e., it is the continuous part of a
quadratic covariation between functions of $C_{t}$, and hence can be
estimated using the estimators proposed in Section (ref).
Consider the first quantity in equation ((ref)),
which is the continuous part of the quadratic covariation between $j^{th}$
and $s^{th}$ IdioVol, $[C_{Zj},C_{Zs}]_{T}^{c}$.
By equation ((ref)), $C_{Z\ell }=C_{Y\ell
,t}-(C_{FS\ell ,t})^{\top }(C_{F,t})^{-1}C_{FS\ell ,t}$, and the quantity is
of the form $[C_{Zj},C_{Zs}]_{T}^{c}=\left[ H\left( C_{t}\right) ,G\left(
C_{t}\right) \right] _{T}^{c}$, where
eqnarray*[eqnarray* omitted — 174 chars of source]
As per equation ((ref)), $Corr\left( C_{Zj},C_{Zs}\right) $ is also of
the form of equation ((ref)).
Therefore, $Corr\left( C_{Zj},C_{Zs}\right) $ is
of the form of equation ((ref)).
\fi
Next, note that IdioVol-FM implies
eqnarray[eqnarray omitted — 374 chars of source]
for $j,s=1,\ldots ,d_{S}$. Recall that $C_{Zj,t}$, $C_{Zs,t}$, and every
element of $\Pi _{t}$ are given real-valued functions of $C_{t}$.
For example, if volatility factors are the volatilities of
return factors $F_t$, we have $\Pi \left( C_{t}\right) =C_{F,t}$, so $\Pi
\left( \cdot \right) $ selects the last $d_{F}$ diagonal elements from $
C_{t}$ (recall that $F_{t}$ are the last $d_{F}$ elements of vector $Y_t$).
Thus, the
right-hand-sides of ((ref)) and ((ref)) have
the form of equation ((ref)) for a finite number of
quantities of the form $[H_{r}(C),G_{r}(C)]_{T}^{c}$.
Finally, the remaining quantities in equation ((ref)), $Corr\left(C^{resid}_{Zj},C^{resid}_{Zs} \right)$, $Q^{\textit{IdioVol-FM}
}_{Zj,Zs}$ and $R^{2,\textit{IdioVol-FM}}_{Zj}$, are smooth functions of $
[C_{Zj}^{resid}, C_{Zj}^{resid}]^c_T$, $[C_{Zj}, C_{Zs}]^c_T$, ${\Greekmath 010D}_{Zj}$
, and $[\Pi,\Pi]^c_T$, each of which is of the form of equation ((ref)), and hence are themselves of the form of equation (
(ref)).
Asymptotic Properties
In this section, we first present the full list of assumptions for our
asymptotic results. We then obtain the joint asymptotic distribution between
the general functionals $[H_r(C),G_r(C)]^c_T$ for $r=1,\ldots,{\Greekmath 0114}$
introduced in Section (ref). We also
develop estimators for the asymptotic variance-covariance matrix. The
asymptotic distributions of the estimators of $Corr\left(C_{Zi},C_{Zj}
\right) $ and other quantities of interest in Section (ref) follow
by the Delta method (see Section (ref) for
details). Finally, to illustrate the application of the general theory, we
describe three statistical tests about the IdioVols, which we later
implement in the empirical and Monte Carlo analysis.
Assumptions
Recall that the $d$-dimensional process $Y_t$ represents the (log) prices of
stocks, $S_t$, and factors $F_t$.
assumptionSuppose $Y$ is an It\^{o} semimartingale on a filtered
space $(\Omega ,\mathcal{F},(\mathcal{F}_{t})_{t\geq 0},\mathbb{P})$,
\begin{equation}
Y_{t}=Y_{0}+\int_{0}^{t}b_{s}ds+\int_{0}^{t}{\Greekmath 011B}
_{s}dW_{s}+\int_{0}^{t}\int_{E}{\Greekmath 010E} (s,z){\Greekmath 0116} (ds,dz),
\end{equation}
where $W$ is a $d^{W}$-dimensional Brownian motion ($d^{W}\geq d$) and ${\Greekmath 0116} $
is a Poisson random measure on $\mathbb{R}_{+}\times E$, with $E$ an
auxiliary Polish space with intensity measure ${\Greekmath 0117} (dt,dz)=dt\otimes {\Greekmath 0115}
(dz)$ for some ${\Greekmath 011B} $-finite measure ${\Greekmath 0115} $ on $E$. The process $
b_{t} $ is $\mathbb{R}^{d}$-valued optional, ${\Greekmath 011B} _{t}$ is $\mathbb{R}
^{d}\times \mathbb{R}^{d^{W}}$-valued, and ${\Greekmath 010E} ={\Greekmath 010E} (w,t,z)$ is a
predictable $\mathbb{R}^{d}$ -valued function on $\Omega \times \mathbb{R}
_{+}\times E$. Moreover, $\Vert {\Greekmath 010E} (w,t\wedge {{\Greekmath 011C} _{m}(w)},z)\Vert
\wedge 1\leq \Gamma _{m}(z)$, for all (w,t,z), where (${\Greekmath 011C} _{m}$) is a
localizing sequence of stopping times and, for some $r\in \lbrack 0,1/2)$,
the function $\Gamma _{m}$ on $E$ satisfies $\int_{E}\Gamma
_{m}(z)^{r}{\Greekmath 0115} (dz)<\infty $. The spot volatility matrix of $Y$ is then
defined as $C_{t}={\Greekmath 011B} _{t}{\Greekmath 011B} _{t}^{\top }$. We assume that $C_{t}$ is
an It\^{o} semimartingale,\footnote{
Note that $\widetilde{{\Greekmath 011B} }_{s}=(\widetilde{{\Greekmath 011B} }_{s}^{gh,m})$ is $
(d\times d\times d^{W})$-dimensional and $\widetilde{{\Greekmath 011B} }_{s}dW_{s}$ is $
(d\times d)$-dimensional with $(\widetilde{{\Greekmath 011B} }_{s}dW_{s})^{gh}=
\sum_{m=1}^{d^{W}}\widetilde{{\Greekmath 011B} }_{s}^{gh,m}dW_{s}^{m}$.}
\begin{equation}
C_{t}=C_{0}+\int_{0}^{t}\widetilde{b}_{s}ds+\int_{0}^{t}\widetilde{{\Greekmath 011B} }
_{s}dW_{s}+J_{t}^{{\Greekmath 011B} },
\end{equation}
where $\widetilde{b}$ is $\mathbb{R}^{d}\times \mathbb{R}^{d}$-valued
optional, and $J_{t}^{{\Greekmath 011B} }$ is a finite activity jump process. $C_{t}$
takes values in the space $\mathcal{M}_{d}$ consisting of $d\times d$
positive definite matrices. For a sequence of convex compact subsets $(
\mathcal{K}_{m})_{m\geq 1}$ of $\mathcal{M}_{d}$, $C_{t}\in \mathcal{K}_{m}$
for all $t\leq {\Greekmath 011C} _{m}$.
With the above notation, the elements of the spot volatility of volatility
matrix and spot covariation of the continuous martingale parts of $X$ and $c$
are defined as follows,
equation[equation omitted — 289 chars of source]
We assume the following for the process $\widetilde{{\Greekmath 011B} }_{t}$:
assumption$\widetilde{{\Greekmath 011B} }_{t}$ is a continuous It\^{o}
semimartingale with its characteristics satisfying the same requirements as
that of $C_{t}-J_{t}^{{\Greekmath 011B} }$.
Assumption (ref) is very general and nests most of the
multivariate continuous-time models used in economics and finance. It allows
for potential stochastic volatility and jumps in returns. Assumption (ref) is required to obtain the asymptotic distribution of estimators
of the quadratic covariation between functionals of the spot covariance
matrix $C_t$. It is not needed to prove consistency. This assumption also
appears in WangMykland12, vetter-vovo, and kalninaxiu-lev.
Asymptotic Distribution
We have seen in Section (ref) that all quantities of interest
in ((ref)) are functions of multiple objects of the
form $[H(C),G(C)]^c_T$. Therefore, if we can obtain a multivariate
asymptotic distribution for a vector with elements of the form $
[H(C),G(C)]^c_T$, the asymptotic distributions for all our estimators follow
by the Delta method. The current section presents this asymptotic
distribution.
Let $H_{1},G_{1},\ldots ,H_{{\Greekmath 0114} },G_{{\Greekmath 0114} }$ be given smooth
real-valued functions. We are interested in the asymptotic behavior of
vectors
equation[equation omitted — 377 chars of source]
The following theorem summarizes the joint asymptotic behavior of the
estimators.
theoremLet $\widehat{[H_{r}(C),G_{r}(C)]_{T}^{c}}$ denote either $
\widehat{[H_{r}(C),G_{r}(C)]_{T}^{c}}^{AN}$ or $\widehat{
[H_{r}(C),G_{r}(C)]_{T}^{c}}^{LIN}$ defined in equations ((ref))
and ((ref)), where $H_{r}$ and $G_{r}$ are three times
differentiable real-valued functions, for $r=1,\ldots ,{\Greekmath 0114} $.
Suppose Assumptions (ref) and (ref) hold.
Fix $k_{n}={\Greekmath 0112} \Delta _{n}^{-1/2}$ for some ${\Greekmath 0112} \in (0,\infty )$.
Set $u_{n}\asymp \Delta _{n}^{{\Greekmath 0124} }$
with
$\frac{2{\Greekmath 0124}^{\prime }+9}{4\left( 5-r\right) }
<
{\Greekmath 0124}
<\frac{1}{2}$, and $
u_{n}^{\prime }\asymp \Delta _{n}^{{\Greekmath 0124} ^{\prime }}$
with
$0 < {\Greekmath 0124} ^{\prime }<\min \left( \frac{1}{2}-r,\frac{1}{8}\right) $
. Then, as $
\Delta _{n}\rightarrow 0$,
\begin{equation}
\Delta _{n}^{-1/4}\left(
\begin{array}{c}
\widehat{\lbrack H_{1}(C),G_{1}(C)]_{T}^{c}}-[H_{1}(C),G_{1}(C)]_{T}^{c} \\
\ldots \\
\widehat{\lbrack H_{{\Greekmath 0114} }(C),G_{{\Greekmath 0114} }(C)]_{T}^{c}}-[H_{{\Greekmath 0114}
}(C),G_{{\Greekmath 0114} }(C)]_{T}^{c}
\end{array}
\right) \overset{\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{L-s}}{\longrightarrow }MN(0,\Sigma _{T}).
\end{equation}
Let $\Sigma _{T}^{r,s}$ be the $\left( \Sigma _{T}\right) _{r,s}$ element of
the ${\Greekmath 0114} \times {\Greekmath 0114} $ matrix $\Sigma _{T}$. We have
\begin{align*}
& \Sigma _{T}^{r,s}=\Sigma _{T}^{r,s,(1)}+\Sigma _{T}^{r,s,(2)}+\Sigma
_{T}^{r,s,(3)}, \\
& \Sigma _{T}^{r,s,(1)}=\frac{6}{{\Greekmath 0112} ^{3}}\sum_{g,h,a,b=1}^{d}
\sum_{j,k,l,m=1}^{d}\int_{0}^{T}\big(\partial _{gh}H_{r}\partial
_{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s}(C_{s})\big)\Big[
C_{t}(gh,jk)C_{t}(ab,lm) \\
& +C_{t}(ab,jk)C_{t}(gh,lm)\Big]dt, \\
& \Sigma _{T}^{r,s,(2)}=\frac{151{\Greekmath 0112} }{140}\sum_{g,h,a,b=1}^{d}
\sum_{j,k,l,m=1}^{d}\int_{0}^{T}\big(\partial _{gh}H_{r}\partial
_{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s}(C_{t})\big)\Big[\overline{C}
_{t}^{gh,jk}\overline{C}_{t}^{ab,lm} \\
& +\overline{C}_{t}^{ab,jk}\overline{C}_{t}^{gh,lm}\Big]dt, \\
& \Sigma _{T}^{r,s,(3)}=\frac{3}{2{\Greekmath 0112} }\sum_{g,h,a,b=1}^{d}
\sum_{j,k,l,m=1}^{d}\int_{0}^{T}\big(\partial _{gh}H_{r}\partial
_{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s}(C_{t})\big)\Big[C_{t}(gh,jk)
\overline{C}_{t}^{ab,lm} \\
& +C_{t}(ab,lm)\overline{C}_{t}^{gh,jk}+C_{t}(gh,lm)\overline{C}
_{t}^{ab,jk}+C_{t}(ab,jk)\overline{C}_{t}^{gh,lm}\Big]dt,
\end{align*}
with
\begin{equation*}
C_{t}(gh,jk)=C_{gj,t}C_{hk,t}+C_{gk,t}C_{hj,t}.
\end{equation*}
The convergence in Theorem (ref) is stable in law (denoted $L$-$s$,
see for example AldousEagle78 and jacodprotter2012). The limit
is mixed gaussian and the precision of the estimators depends on the paths
of the spot covariance and the volatility of volatility process. The rate of
convergence $\Delta_n^{-1/4}$ has been shown to be the optimal for
volatility of volatility estimation (in the absence of volatility jumps).
The asymptotic variance of the estimators depends on the tuning parameter $
{\Greekmath 0112}$ whose choice may be crucial for the reliability of the inference. We
document the sensitivity of the inference theory to the choice of the
parameter ${\Greekmath 0112}$ in a Monte Carlo experiment (see Section (ref)).
Estimation of the Asymptotic Covariance Matrix
To provide a consistent estimator for the element $\Sigma _{T}^{r,s}$ of the
asymptotic covariance matrix in Theorem (ref), we introduce the
following quantities:
align*[align* omitted — 2,293 chars of source]
with
$\widehat{{\Greekmath 0115} }_{i}^{n,jk}=\widehat{C}_{i+k_{n}}^{n,jk}-\widehat{C}
_{i}^{n,jk}$,
$\widetilde{C}_{i\Delta _{n}}(gh,jk)=
\widehat{C}_{gj,i\Delta_{n}}\widehat{C}_{hk,i\Delta _{n}}
+
\widehat{C}_{gk,i\Delta _{n}}\widehat{C}_{hj,i\Delta _{n}}$,
and
$A_{i}=\{||\widehat{C}_{\left( i+k_{n}\right) \Delta _{n}}-\widehat{C}_{\left( i-k_{n}\right) \Delta _{n}}||<u_{n}^{\prime }\} $.
The following result holds,
theoremSuppose the assumptions of Theorem (ref) hold.
Then, as $\Delta _{n}\rightarrow 0$,
\begin{align}
& \frac{6}{{\Greekmath 0112} ^{3}}\widehat{\Omega }_{T}^{r,s,(1)}\overset{\mathbb{P}}{
\longrightarrow }\Sigma _{T}^{r,s,(1)}, \\
& \frac{3}{2{\Greekmath 0112} }[\widehat{\Omega }_{T}^{r,s,(3)}-\frac{6}{{\Greekmath 0112} }
\widehat{\Omega }_{T}^{r,s,(1)}]\overset{\mathbb{P}}{\longrightarrow }\Sigma
_{T}^{r,s,(3)},\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ and} \\
& \frac{151{\Greekmath 0112} }{140}\frac{9}{4{\Greekmath 0112} ^{2}}[\widehat{\Omega }
_{T}^{r,s,(2)}+\frac{4}{{\Greekmath 0112} ^{2}}\widehat{\Omega }_{T}^{r,s,(1)}-\frac{4}{
3}\widehat{\Omega }_{T}^{r,s,(3)}]\overset{\mathbb{P}}{\longrightarrow }
\Sigma _{T}^{r,s,(2)}.
\end{align}
The estimated matrix $\widehat{\Sigma}_T$ is symmetric but is not guaranteed
to be positive semi-definite. By Theorem (ref), $\widehat{\Sigma}_T$
is positive semi-definite in large samples.
An interesting question is the estimation of the asymptotic variance using
subsampling or bootstrap methods, see kalnina11-sub,kalnina23-multisub
, and we leave it for future research.
Remark 1: The rate of convergence in equation ((ref)) can
be shown to be $\Delta_n^{-1/2}$, and the rate of convergence in ((ref)) and ((ref)) can be shown to be $\Delta_n^{-1/4}$.
Remark 2: In the one-dimensional case ($d=1$), much simpler
estimators of $\Sigma_T^{r,s,(2)}$ can be constructed using the quantities $
\widehat{{\Greekmath 0115}}_i^{n,jk}\widehat{{\Greekmath 0115}}_i^{n,lm}\widehat{{\Greekmath 0115}}
_{i+k_n}^{n,gh}\widehat{{\Greekmath 0115}}_{i+k_n}^{n,xy}$ or $\widehat{{\Greekmath 0115}}
_i^{n,jk}\widehat{{\Greekmath 0115}}_i^{n,lm}\widehat{{\Greekmath 0115}}_{i}^{n,gh}\widehat{
{\Greekmath 0115}}_{i}^{n,xy}$ as in vetter-vovo. However, in the
multidimensional case, the latter quantities do not identify separately the
quantity $\overline{C_t}^{jk,lm}\overline{C_t}^{gh,xy}$ since the
combination $\overline{C_t}^{jk,lm}\overline{C_t}^{gh,xy}+\overline{C_t}
^{jk,gh}\overline{C_t}^{lm,xy}+\overline{C_t}^{jk,xy}\overline{C_t}^{gh,lm}$
shows up in a non-trivial way in the limit of the estimator.
corollaryLet $\widehat{[H_{r}(C),G_{r}(C)]_{T}^{c}}$ denote either $\widehat{
[H_{r}(C),G_{r}(C)]_{T}^{c}}^{AN}$ or $\widehat{[H_{r}(C),G_{r}(C)]_{T}^{c}}
^{LIN}$ defined in equations ((ref)) and ((ref)).
Suppose the assumptions of Theorem (ref) hold. Then, as $\Delta
_{n}\rightarrow 0$,
\begin{equation}
\Delta _{n}^{-1/4}\ \widehat{\Sigma }_{T}^{-1/2}\left(
\begin{array}{l}
\widehat{\lbrack H_{1}(C),G_{1}(C)]_{T}^{c}}-[H_{1}(C),G_{1}(C)]_{T}^{c} \\
\vdots \\
\widehat{\lbrack H_{{\Greekmath 0114} }(C),G_{{\Greekmath 0114} }(C)]_{T}^{c}}-[H_{{\Greekmath 0114}
}(C),G_{{\Greekmath 0114} }(C)]_{T}^{c}
\end{array}
\right) \overset{L}{\longrightarrow }N(0,I_{{\Greekmath 0114} }).
\end{equation}
In the above, we use $L$ to denote the convergence in distribution and $
I_{{\Greekmath 0114}}$ the identity matrix of order ${\Greekmath 0114}$. Corollary (ref)
states the standardized asymptotic distribution, which follows directly from
the properties of the stable-in-law convergence. Similarly, by the Delta
method, standardized asymptotic distribution can also be derived for the
estimators of the quantities in ((ref)). These
standardized distributions allow the construction of confidence intervals
for all the latent quantities of the form $[H_r(C),G_r(C)]^c_T$ and, more
generally, functions of these quantities.
Tests
As an illustration of application of the general theory, we provide three
tests about the dependence of Idiosyncratic Volatility. Our framework allows
to test general hypotheses about the joint dynamics of any subset of the
available stocks. The three examples below are stated for one pair of
stocks, and correspond to the tests we implement in the empirical and Monte
Carlo studies.
First, one can test for the absence of dependence between the continuous
components of the IdioVols of the returns on assets $j$ and $s$,
equation[equation omitted — 85 chars of source]
Under $H_{0}^{1}$, $\Delta _{n}^{-1/4}\widehat{[C_{Zj},C_{Zs}]_{T}^{c}}
\widehat{V}^{-1/2}\overset{L}{\rightarrow }N\left( 0,1\right) $, so we can
use a t-test.
Second, we can test the hypothesis that none of the IdioVol factors $\Pi $
explaining the dynamics of IdioVol shocks of stock $j$,
equation[equation omitted — 91 chars of source]
Under this null hypothesis, the vector of IdioVol factor loadings equals
zero, ${\Greekmath 010D} _{Z_{j}}=0$. Under $H_{0}^{2}$,
equation[equation omitted — 255 chars of source]
so we can use a Wald test. One can of course also construct a t-test for
irrelevance of any one particular IdioVol factor. The final example is a
test for absence of dependence between the residual IdioVols of stock $j$
and $s$,
equation[equation omitted — 106 chars of source]
Under $H_{0}^{1}$, $\Delta _{n}^{-1/4}\widehat{
[C_{Zj}^{resid},C_{Zs}^{resid}]_{T}^{c}}\widehat{V}^{-1/2}\overset{L}{
\rightarrow }N\left( 0,1\right) $, so we can use a t-test.
Each of the above estimators
equation*[equation* omitted — 212 chars of source]
can be obtained by choosing appropriate pair(s) of transformations $H$ and $
G $ in the general estimator $\widehat{[H(C),G(C)]_{T}^{c}}$, see Section
(ref) for details. Any of the two types of the latter
estimator can be used,
equation*[equation* omitted — 170 chars of source]
For the first two tests, the expression for the true asymptotic variance, $V$
, is obtained using Theorem (ref) and its estimation follows from
Theorem (ref). The asymptotic variance in the third test is
obtained by applying the Delta method to the joint convergence result in
Theorem (ref). The expression for the estimator of the asymptotic
variance, $\widehat{V}$, follows from Theorem (ref).
Under R-FM and the assumptions of Theorem (ref), Corollary (ref) implies that the asymptotic size of the two types of tests for the
null hypotheses $H_{0}^{1}$ and $H_{0}^{2}$ is ${\Greekmath 010B} $, and their power
approaches 1. The same properties apply for the tests of the null hypotheses
$H_{0}^{3}$ with our R-FM and IdioVol-FM representations.
Theoretically, it is possible to test for absence of dependence in the
IdioVols at each point in time. In this case the null hypothesis is $
H_{0}^{1\prime }:[C_{Zj},C_{Zs}]_{t}^{c}=0\hspace{3mm}\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for all}\hspace{
3mm}0\leq t\leq T$, which is, in theory, stronger than our $H_{0}^{1\prime }$
. In particular, Theorem (ref) can be used to set up
Kolmogorov-Smirnov type of tests for $H_{0}^{\prime 1}$ in the same spirit
as vetter-vovo. However, we do not pursue this direction in the
current paper for two reasons. First, the testing procedure would be more
involved. Second, empirical evidence suggests nonnegative dependence between
IdioVols, which means that in practice, it is not too restrictive to assume $
[C_{Zj},C_{Zs}]_{t}^{c}\geq 0~\forall t$, under which $H_{0}^{1}$ and $
H_{0}^{1\prime }$ are equivalent.
Empirical Analysis
We apply our methods to study the cross-sectional dependence in IdioVols
using high frequency data. One of our main findings is that stocks' IdioVols
co-move strongly with the market volatility. This is a quite surprising
finding. It is of course well known that the total volatility of stocks
moves with the market volatility. However, we stress that we find that the
strong effect is still present when considering the IdioVols.
We use transaction prices from NYSE TAQ database for S&P 100 index
constituents from 2003 to 2012. Starting with the union of constituents over
this period, we select only those stocks for which complete data is
available; this results in a full sample of 104 stocks. After excluding the
non-trading days, our sample contains 2517 days. We also use the
high-frequency data on nine industry Exchange-Traded Funds, ETFs (Consumer
Discretionary, Consumer Staples, Energy, Financial, Health Care, Industrial,
Materials, Technology, and Utilities), and the high-frequency size and value
Fama-French factors, see yackalninaxiu-FF. To aid visualization, we
report additional results for a subset of 30 stocks. We obtain the subset of
30 stocks by selecting at least two stocks from each of the nine GICS
sectors, together with the most liquid stocks; see Table (ref)
for details. For each day, we consider data from the regular exchange
opening hours from time stamped between 9:30 a.m. until 4 p.m.
We clean the data following the procedure suggested by barndorffnielsenhansenlundeshephard08, remove the overnight returns and
then sample at 5 minutes. This sparse sampling has been widely used in the
literature because the effect of the microstructure noise and potential
asynchronicity of the data is less important at this frequency, see also
LiuPattonSheppard15. The return jump truncation threshold is the same
as in simulations, see Section (ref). The number of observations in
the local window is taken as in Theorem (ref) to be $k_{n}={\Greekmath 0112}
\Delta _{n}^{-1/2}$. We take ${\Greekmath 0112} =2.5$ and $\Delta _{n}=1/252/(6.5\times
12)$, i.e., $\Delta _{n}$ is 5 minutes (with one year being a unit of time),
which corresponds to the local window of approximately one week. The
threshold for volatility jumps is based on the individual asset volatility
changing by more than 10 percentage points. The optimal selection of this
tuning parameter is a complex issue that falls outside the scope of this
paper. We find that both
types of estimators, AN and LIN, produce very similar results and report
only the AN estimator for brevity.
To obtain the Idiosyncratic Volatilities, the preliminary step is to
estimate the Return Factor Model (R-FM) for each stock. Figures (ref) and (ref) contain plots of the time series of the
estimated $R_{Yj}^{2}$ of the R-FM for the subset of 30 stocks.\footnote{
For the $j^{th}$ stock, our analog of the coefficient of determination in
the R-FM is $R_{Yj}^{2}=1-\frac{\int_{0}^{T}C_{Zj,t}dt}{
\int_{0}^{T}C_{Yj,t}dt}$.
We estimate $R_{Yj}^{2}$ using the general method of jacodrosenbaum13
. The resulting estimator of $R_{Yj}^{2}$ requires a choice of a block size
for the spot volatility estimation; we choose two hours in practice (the
number of observations in a block, say $l_{n}$, has to satisfy $
l_{n}^{2}\Delta _{n}\rightarrow 0$ and $l_{n}^{3}\Delta _{n}\rightarrow
\infty $, so it is of smaller order than the number of observations $k_{n}$
in our estimators of Section (ref)).} Each plot contains
monthly $R_{Yj}^{2}$ from two Return Factor Models, CAPM and the Fama-French
regression with market, size, and value factors.
Figures (ref) and (ref) show that these time series of all
stocks follow approximately the same trend with a considerable increase in
the contribution around the crisis year 2008. Higher $R_{Yj}^{2}$ indicates
that the systematic risk is relatively more important, which is typical
during crises. $R_{Yj}^{2}$ is consistently higher in the Fama-French
regression model compared to the CAPM regression model, albeit not by much.
We proceed to investigate the dynamic properties of the panel of
Idiosyncratic Volatilities.
We first investigate the dependence in the (total) Idiosyncratic
Volatilities. Our panel has 5356
pairs of stocks. For each pair of stocks, we compute the correlation between
the IdioVols, $Corr\left( C_{Zi},C_{Zj}\right) $, see Section (ref) for the implementation details. All pairwise
correlations are positive in our sample, and their average is $0.35$. Figure
(ref) contains a heatmap of this dependency measure in the
IdioVols. We simultaneously test 5356 hypotheses of no correlation, and
Figure (ref) assigns non-zero correlations only for those
pairs of assets, for which the null is rejected; the diagonal contains
zeros, too. We account for multiple testing by controlling the false
discovery rate at $5\%$. Overall, Figure (ref) shows that
the cross-sectional dependence between the IdioVols is very strong.\ To aid
visualization, Figure (ref) maps the network of dependencies
in the IdioVols for the subset of 30 stocks. Similarly to Figure (ref), in Figure (ref), we simultaneously test
435 hypotheses of no correlation,
and Figure (ref) connects only the assets, for which the
null is rejected. Unsurprisingly, the cross-sectional dependence between the
IdioVols is also very strong among this subset of stocks.
figure[figure omitted — 625 chars of source]
figure[figure omitted — 661 chars of source]
Could missing factors in the R-FM provide an explanation? Omitted return
factors in the R-FM are captured by the idiosyncratic returns, and can
therefore induce correlation between the estimated IdioVols, provided these
missing return factors have non-negligible volatility of volatility. To
investigate this possibility, we consider the correlations between
idiosyncratic returns, $Corr(Z_{i},Z_{j})$.\footnote{
Our measure of correlation between the idiosyncratic returns $dZ_{i}$ and $
dZ_{j}$ is
equation[equation omitted — 181 chars of source]
where $C_{ZiZj,t}$ is the spot covariation between
$Z_{i}$ and $Z_{j}$.
Similarly to $R_{Yj}^{2}$, we estimate $Corr(Z_{i},Z_{j})$ using the
estimator of jacodrosenbaum13.} Table (ref)
presents a summary of how estimates of $Corr(Z_{i},Z_{j})$ are related to
the estimates of correlation in IdioVols, Corr$\left(
C_{Zi},C_{Zj}\right) $. In particular, different rows in Table (ref) display average values of $\widehat{Corr}\left(
C_{Zi},C_{Zj}\right) $ among those pairs, for which $|\widehat{Corr}
(Z_{i},Z_{j})|$ is below some threshold.
We observe that even among pairs with virtually
uncorrelated idiosyncratic returns, the correlations among IdioVols are
still high. This conclusion holds both for the idiosyncratic returns and
volatilities defined with respect to CAPM, as well as the R-FM with three
Fama-French factors. Moreover, we observe that IdioVol correlations, $
\widehat{Corr}\left( C_{Zi},C_{Zj}\right) $, are similar compared among
pairs that have high or low idiosyncratic return correlations, $\widehat{Corr
}\left( C_{Zi},C_{Zj}\right) $. These results suggest that missing return
factors cannot explain dependence in IdioVols for all considered stocks.
This finding is in line with the empirical analysis of herskovickellyCIV with daily and monthly returns.
To understand the source of the strong cross-sectional dependence in the
IdioVols, we consider the Idiosyncratic Volatility Factor Model (IdioVol-FM)
of Section (ref). We first use the market volatility as the only
IdioVol factor ($d_{\Pi}=1$).\footnote{
We also considered the volatility of size and value Fama-French factors.
However, both these factors turned out to have very low volatility of
volatility and therefore did not significantly change the results.}
Panel (a) of Table (ref)
reports the estimates of the IdioVol loading ($
\widehat{{\Greekmath 010D} }_{Zi}$) and the $R^{2}$ of the IdioVol-FM ($R_{Zi}^{2,
\textit{IdioVol-FM}}$, see equation ((ref))).
Panel (a) uses two different definitions of IdioVol, one
defined with respect to CAPM, and a second IdioVol defined with respect to
Fama-French three factor model. For virtually every stock, the estimated
IdioVol factor loading is positive, suggesting that the Idiosyncratic
Volatility co-moves with the market volatility. We have also calculated the
relevant t-statistics, showing that for virtually every stock, IdioVol
loading $\widehat{{\Greekmath 010D} }_{Zi}$ is highly statistically significant. Next,
Figures (ref) and (ref) show dependencies
among residual IdioVols after accounting for the market volatility as the sole
IdioVol factor. The average pairwise correlations between the residual
IdioVols, $\widehat{Corr}(C_{Zi}^{resid},C_{Zj}^{resid})$, across all pairs
of stocks,
decrease to $0.21$. However, the market volatility cannot explain all
cross-sectional dependence in residual IdioVols, as evidenced by the
remaining links in both Figure (ref) and (ref).
Finally, we consider an IdioVol-FM with ten IdioVol factors, $d_{\Pi}=10$,
market volatility and the volatilities of nine industry ETFs.
We use CAPM IdioVols.
Panel (b) of Table (ref)
reports the corresponding $R_{Zi}^{2,\textit{IdioVol-FM}}$,
which is considerably higher than in the one-factor case, $d_{\Pi}=1$.
Figures (ref) and (ref) show the implications for the
cross-section of this ten-factor IdioVol-FM,
for 104 and \ 30 stocks, respectively. The average pairwise
correlations between the residual IdioVols, $\widehat{Corr}
(C_{Zi}^{resid},C_{Zj}^{resid})$, decrease further to $0.17$. However,
significant dependence between the residual IdioVols remains, as evidenced
by the remaining links in both Figures (ref) and (ref). Our results suggest that there is room for considering
the construction of additional IdioVol factors based on economic theory, for
example, along the lines of the heterogeneous agents model of herskovickellyCIV.
For comparison, we also calculate the naive estimators, see equation ((ref)). Of course, since the naive estimators are inconsistent, we
do not have valid confidence intervals to accompany them. We focus on the
one-factor IdioVol-FM. In our data set, the absolute values of the
differences between\ the naive and the bias-corrected estimators range,
across all pairs of stocks, between $0$ and $0.045$ for $Corr\left(
C_{Zi},C_{Zj}\right) $, between $0$ and $0.051$ for $
Corr(C_{Zi}^{resid},C_{Zj}^{resid})$, and between $0.06$ and $0.13$ for $
R_{Zj}^{2,\textit{IdioVol-FM}}$. However, the relative errors can be large, for
example, for $R_{Zj}^{2,\textit{IdioVol-FM}}$, it is $42\%$ on average. We find that in the
instances where the differences are small, the multiplicative bias, i.e.,
the factor $2/3$, dominates the additive bias both in the numerator and the
denominator, so that the multiplicative bias approximately cancels out for
these estimands.
figure[figure omitted — 729 chars of source]
figure[figure omitted — 767 chars of source]
figure[figure omitted — 759 chars of source]
figure[figure omitted — 795 chars of source]
table[table omitted — 1,358 chars of source]
sidewaystable[thb]
\begin{center}
\begin{tabular}{lcccccccc}
\hline\hline
& & \multicolumn{3}{c}{\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{CAPM}} & & \multicolumn{3}{c}{\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{FF3 Model}}\\
$|\widehat{\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{Corr}}(Z_i,Z_j)|$ & & Pairs & Avg\,$|\widehat{\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{Corr}}(Z_i,Z_j)|$ &
Avg\,$\widehat{\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{Corr}}\left(C_{Zi},C_{Zj} \right)$
& & Pairs & Avg\,$|\widehat{\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{Corr}}(Z_i,Z_j)|$ &
Avg\,$\widehat{\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{Corr}}\left(C_{Zi},C_{Zj} \right)$
\\
\cline{1-1} \cline{3-5} \cline{7-9}
$<0.6$ & & 5356 & 0.045 & 0.347 & & 5356 & 0.045 & 0.347\\
$<0.5$ & & 5354 & 0.045 & 0.347 & & 5353 & 0.045 & 0.347\\
$<0.4$ & & 5334 & 0.044 & 0.346 & & 5335 & 0.044 & 0.346\\
$<0.3$ & & 5300 & 0.042 & 0.344 & & 5300 & 0.042 & 0.345\\
$<0.2$ & & 5236 & 0.039 & 0.343 & & 5236 & 0.039 & 0.344\\
$<0.1$ & & 4925 & 0.033 & 0.338 & & 4928 & 0.033 & 0.339\\
$<0.075$ & & 4642 & 0.030 & 0.333 & & 4647 & 0.030 & 0.333\\
$<0.050$ & & 3873 & 0.024 & 0.320 & & 3895 & 0.024 & 0.320\\
$<0.025$ & & 2049 & 0.013 & 0.296 & & 2044 & 0.013 & 0.296\\
$<0.010$ & & 757 & 0.005 & 0.293 & & 748 & 0.005 & 0.293\\
$<0.005$ & & 374 & 0.003 & 0.297 & & 373 & 0.002 & 0.296\\
\hline\hline
\end{tabular}
\caption{Each row in this table describes the subset of pairs of stocks with $|\widehat{\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{Corr}(Z_i,Z_j)}|$ below a threshold in column one.
The table considers two R-FMs: the left panel defines the IdioVol with respect to CAPM, and the right panel defines the IdioVol with respect to the three-factor Fama-French model. In both cases, the market volatility is the only IdioVol factor. Each panel reports three quantities for the given subset of pairs: the number of pairs, average absolute pairwise correlation in idiosyncratic returns, and average pairwise correlation between IdioVols.
}
\end{center}
table[table omitted — 6,919 chars of source]
\FloatBarrier
Monte Carlo
This section investigates the finite sample properties of our
estimators and tests. The data generating process (DGP) is similar to that
of litodorovtauchen-dependencies-WP2013 and is constructed as
follows. Denote by $Y_{1}$ and $Y_{2}$ the log-prices of two individual
stocks, and by $X$ the log-price of the market portfolio. Recall that the
superscript $c$ indicates the continuous part of a process. We assume
align*[align* omitted — 81 chars of source]
and, for $j=1,2$,
align*[align* omitted — 157 chars of source]
In the above, $C_{X}$ is the spot volatility of the market portfolio, $
\widetilde{W}_1$ and $\widetilde{W}_2$ are Brownian motions with $\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{Corr}
(d\widetilde{W}_{1,t},d\widetilde{W}_{2,t})=0.4$, and $W$ is an independent
Brownian motion; $J_1, J_2$, and $J_3$ are independent compound Poisson
processes with intensity equal to 2 jumps per year and jump size
distribution $N(0,0.02^2)$. The beta process is time-varying and is
specified as ${\Greekmath 010C}_t=0.5+0.1\hspace{1mm}\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{sin}(100t). $
We next specify the volatility processes. As our building blocks, we first
generate four processes $f_{1},\ldots ,f_{4}$ as mutually independent
Cox-Ingersoll-Ross processes,
align*[align* omitted — 251 chars of source]
where $B_{1},\ldots ,B_{4}$ are independent standard\ Brownian Motions,
which are also independent from the Brownian Motions of the return Factor
Model.\footnote{
The Feller property is satisfied implying the positiveness of the processes $
(f_{j,t})_{1\leq j\leq 4}$.} We use the first process $f_{1}$ as the market
volatility, i.e., $C_{X,t}=f_{1,t}$. We use the other three processes $
f_{2},f_{3}$, and $f_{4}$ to construct two
different specifications for the IdioVol processes $C_{Z1,t}$ and $C_{Z2,t}$
, see Table (ref) for details. The common Brownian Motion $W_{t}$
in the market portfolio price process $X_{t}$ and its volatility process $
C_{X,t}=f_{1,t}$ generates a leverage effect for the market portfolio. The
value of the leverage effect is $-0.8$, which is standard in the literature,
see kalninaxiu-lev, aitfanli13 and Yacine-jump-lev.
\footnote{
Notice that by It\^{o} Lemma, each of these three models can be expressed in
terms of equation ((ref)) for the vector $\left(
X_{t},Y_{1,t},Y_{2,t}\right) ^{\prime }$ and equation ((ref)) for
the volatility matrix of this vector.}
table[table omitted — 449 chars of source]
We set the time span $T$ equal to 1,260 or 2,520 days, which correspond
approximately to 5 and 10 business years. These values are standard in the
nonparametric leverage effect estimation literature (see aitfanli13
and kalninaxiu-lev), where the rate of convergence is also $\Delta
^{-1/4}$. Each day consists of 6.5 trading hours. We consider two different
values for the sampling frequency, $\Delta _{n}=$ 1 minute and $\Delta _{n}=$
5 minutes.
We follow litodorovtauchen-dependencies and set the jump truncation
threshold $u_{n}$ in day $t$ at $3\widehat{{\Greekmath 011B} }_{t}\Delta _{n}^{0.49}$,
where $\widehat{{\Greekmath 011B} }_{t}$ is the squared root of the annualized bipower
variation of barndorffnielsenshephard04. We choose four different
values for the width of the subsamples, which corresponds to ${\Greekmath 0112}
=1.5,2,2.5$ and $3$ (recall that the number of observations in a window is $
k_{n}={\Greekmath 0112} /\sqrt{\Delta _{n}}$). We use 10,000 Monte Carlo replications
in all the experiments.
We first investigate the finite sample properties of the estimators (using
Model 3). We consider the following estimands:
itemize• the IdioVol factor loading of the first stock, ${\Greekmath 010D} _{Z1}$,
• the contribution of the market volatility to the variation of the
IdioVol of the first stock $R_{Z1}^{2,\textit{IdioVol-FM}}$,
• the correlation between the Idiosyncratic Volatilities of stocks 1 and
2, $Corr\left( C_{Z1},C_{Z2}\right) $,
• the correlation between the residual Idiosyncratic Volatilities, $
Corr\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $.
In Table (ref), we report the median bias, the interquartile
range (IQR), and the RMSE of the two type of the bias-corrected estimators
as well as the naive estimator for each estimand using 5 minutes data over
10 years. In Tables (ref)-(ref), in order to simplify the
interpretation of the results, we fix the volatility paths $C_{X,t}$ and $
(f_{j,t})_{0\leq j \leq 4}$ across simulations.
Consider first the comparison of the AN and LIN estimators. One does not
consistently over-perform the other in terms of the bias or the IQR.
Interestingly, in terms of the RMSE, the LIN estimator outperforms the AN
estimator in every scenario considered. The naive estimators are
substantially biased. The comparison of the bias-corrected estimators and
the naive estimators reveals the usual bias-variance trade-off, as the
bias-corrected estimators have smaller bias but larger IQR than the naive
estimator. In terms of RMSE, the bias-corrected estimators generally
outperform the naive estimator: RMSE is significantly lower when estimating $
{\Greekmath 010D}_{Z1}$, $R^{2,\textit{IdioVol-FM}}_{Z1}$, or $Corr\left(C_{Z1},C_{Z2}
\right) $, while the results for $Corr\left(C^{resid}_{Z1},C^{resid}_{Z2}
\right)$ are mixed.
It is also informative to see how these results change when we increase the
sampling frequency. In Table (ref), we report the results with $
\Delta_n=1\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ minute}$ in the same setting. The qualitative conclusions
of Table (ref) remain true in Table (ref). Compared to
Table (ref), the bias and IQR are smaller. However, the magnitude
of the decrease of the IQR is small.
Finally, Table (ref) contains results from same experiment using data
sampled at one minute over 5 years. Despite using more than twice as many
observations than in the first experiment, the precision is not as good. In
other words, increasing the time span is more effective for precision gain
than increasing the sampling frequency.
The qualitative conclusions generally remain the same as in Table (ref).
sidewaystable\begin{tabular*}{1.0\textwidth}{l|@{\extracolsep{\fill}}cccccccccccc}
\hline\hline
\rule{0pt}{15pt}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{} & \multicolumn{4}{c}{LIN} &
\multicolumn{4}{c}{AN} & \multicolumn{4}{c}{Naive} \\
$\widehat{{\Greekmath 0112} }$ & 1.5 & 2 & 2.5 & 3 & 1.5 & 2 & 2.5 & 3 & 1.5 & 2 & 2.5
& 3 \\ \hline
& & & & & & & & & & & & \\
& \multicolumn{12}{c}{Median Bias} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & -0.007 & -0.004 & -0.005 & -0.011
& -0.032 & -0.027 & -0.025 & -0.028 & -0.257 & -0.230 & -0.209 & -0.177 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & -0.153 & -0.138
& -0.127 & -0.115 & -0.146 & -0.132 & -0.121 & -0.110 & -0.484 & -0.465 &
-0.448 & -0.417 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & -0.129 &
-0.104 & -0.086 & -0.059 & -0.147 & -0.118 & -0.100 & -0.070 & -0.342 &
-0.334 & -0.325 & -0.307 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& -0.089 & -0.064 & -0.045 & -0.018 & -0.109 & -0.082 & -0.061 & -0.029 &
-0.245 & -0.239 & -0.232 & -0.218 \\
& \multicolumn{12}{c}{\rule{0pt}{15pt}IQR} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & 0.173 & 0.157 & 0.141 & 0.118 &
0.173 & 0.154 & 0.140 & 0.118 & 0.079 & 0.078 & 0.078 & 0.076 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & 0.180 & 0.166 &
0.154 & 0.133 & 0.201 & 0.185 & 0.170 & 0.141 & 0.040 & 0.042 & 0.044 & 0.046
\\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & 0.279 & 0.257
& 0.238 & 0.211 & 0.321 & 0.289 & 0.266 & 0.229 & 0.039 & 0.041 & 0.043 &
0.048 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& 0.330 & 0.304 & 0.280 & 0.249 & 0.381 & 0.344 & 0.311 & 0.273 & 0.040 &
0.042 & 0.044 & 0.049 \\
& \multicolumn{12}{c}{\rule{0pt}{15pt}RMSE} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & 0.130 & 0.116 & 0.105 & 0.090 &
0.132 & 0.118 & 0.108 & 0.093 & 0.263 & 0.238 & 0.217 & 0.185 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & 0.206 & 0.185 &
0.170 & 0.150 & 0.242 & 0.192 & 0.174 & 0.152 & 0.484 & 0.466 & 0.449 & 0.418
\\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & 0.257 & 0.226
& 0.203 & 0.169 & 0.309 & 0.260 & 0.229 & 0.187 & 0.343 & 0.335 & 0.327 &
0.309 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& 0.300 & 0.261 & 0.235 & 0.199 & 0.394 & 0.309 & 0.266 & 0.213 & 0.247 &
0.241 & 0.234 & 0.221 \\ \\ \hline\hline
\end{tabular*}
{\caption{Finite sample properties of our estimators using 10 years of data sampled at 5 minutes. The true values are ${\Greekmath 010D}_{Z1}=0.450$,
$R^{2,\textit{IdioVol-FM}}_{Z1}=0.342$, $Corr \left( C_{Z1},C_{Z2} \right)=0.523$,
$Corr\left(C^{resid}_{Z1},C^{resid}_{Z2} \right)=0.424$. Model 2.
}}
sidewaystable\begin{tabular*}{1.0\textwidth}{l|@{\extracolsep{\fill}}cccccccccccc}
\hline\hline
\rule{0pt}{15pt}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{} & \multicolumn{4}{c}{LIN} &
\multicolumn{4}{c}{AN} & \multicolumn{4}{c}{Naive} \\
$\widehat{{\Greekmath 0112} }$ & 1.5 & 2 & 2.5 & 3 & 1.5 & 2 & 2.5 & 3 & 1.5 & 2 & 2.5
& 3 \\ \hline
& & & & & & & & & & & & \\
& \multicolumn{12}{c}{Median Bias} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & -0.034 & -0.029 & -0.022 & -0.013
& -0.052 & -0.044 & -0.036 & -0.025 & -0.304 & -0.295 & -0.275 & -0.267 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & -0.140 & -0.123
& -0.109 & -0.086 & -0.135 & -0.117 & -0.103 & -0.080 & -0.496 & -0.492 &
-0.477 & -0.473 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & -0.146 &
-0.128 & -0.114 & -0.091 & -0.163 & -0.143 & -0.129 & -0.104 & -0.327 &
-0.323 & -0.321 & -0.317 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& -0.118 & -0.105 & -0.095 & -0.076 & -0.138 & -0.123 & -0.111 & -0.092 &
-0.220 & -0.216 & -0.216 & -0.212 \\
& \multicolumn{12}{c}{\rule{0pt}{15pt}IQR} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & 0.147 & 0.132 & 0.118 & 0.100 &
0.146 & 0.131 & 0.117 & 0.099 & 0.062 & 0.062 & 0.063 & 0.063 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & 0.165 & 0.148 &
0.137 & 0.119 & 0.176 & 0.158 & 0.145 & 0.125 & 0.032 & 0.032 & 0.034 & 0.034
\\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & 0.260 & 0.232
& 0.209 & 0.175 & 0.287 & 0.249 & 0.224 & 0.188 & 0.032 & 0.032 & 0.033 &
0.033 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& 0.312 & 0.280 & 0.254 & 0.211 & 0.341 & 0.303 & 0.273 & 0.225 & 0.032 &
0.032 & 0.033 & 0.033 \\
& \multicolumn{12}{c}{\rule{0pt}{15pt}RMSE} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & 0.115 & 0.102 & 0.091 & 0.076 &
0.121 & 0.106 & 0.095 & 0.078 & 0.307 & 0.299 & 0.279 & 0.271 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & 0.192 & 0.165 &
0.147 & 0.121 & 0.198 & 0.168 & 0.148 & 0.121 & 0.496 & 0.493 & 0.478 & 0.474
\\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & 0.251 & 0.220
& 0.196 & 0.162 & 0.283 & 0.243 & 0.215 & 0.177 & 0.328 & 0.324 & 0.322 &
0.318 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& 0.291 & 0.249 & 0.221 & 0.182 & 0.760 & 0.279 & 0.245 & 0.199 & 0.221 &
0.218 & 0.218 & 0.214 \\
\\ \hline\hline
\end{tabular*}
{\caption{Finite sample properties of our estimators using 10 years of data sampled at 1 minute. The true values are ${\Greekmath 010D}_{Z1}=0.450$,
$R^{2,\textit{IdioVol-FM}}_{Z1}=0.336$, $Corr \left( C_{Z1},C_{Z2} \right)=0.514$, $Corr\left(C^{resid}_{Z1},C^{resid}_{Z2} \right)=0.408$. Model 2.}}
sidewaystable\begin{tabular*}{1.0\textwidth}{l|@{\extracolsep{\fill}}cccccccccccc}
\hline\hline
\rule{0pt}{15pt}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{} & \multicolumn{4}{c}{LIN} &
\multicolumn{4}{c}{AN} & \multicolumn{4}{c}{Naive} \\
$\widehat{{\Greekmath 0112} }$ & 1.5 & 2 & 2.5 & 3 & 1.5 & 2 & 2.5 & 3 & 1.5 & 2 & 2.5
& 3 \\ \hline
& & & & & & & & & & & & \\
& \multicolumn{12}{c}{Median Bias} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & -0.075 & -0.072 & -0.068 & -0.061
& -0.096 & -0.089 & -0.083 & -0.075 & -0.323 & -0.315 & -0.299 & -0.291 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & -0.183 & -0.169
& -0.155 & -0.139 & -0.183 & -0.169 & -0.156 & -0.137 & -0.500 & -0.496 &
-0.484 & -0.480 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & -0.187 &
-0.169 & -0.161 & -0.145 & -0.214 & -0.194 & -0.185 & -0.166 & -0.321 &
-0.316 & -0.317 & -0.313 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& -0.144 & -0.128 & -0.125 & -0.116 & -0.167 & -0.155 & -0.146 & -0.139 &
-0.209 & -0.205 & -0.207 & -0.202 \\
& \multicolumn{12}{c}{\rule{0pt}{15pt}IQR} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & 0.229 & 0.205 & 0.184 & 0.154 &
0.225 & 0.202 & 0.184 & 0.154 & 0.092 & 0.092 & 0.093 & 0.093 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & 0.246 & 0.223 &
0.206 & 0.177 & 0.265 & 0.238 & 0.218 & 0.187 & 0.047 & 0.047 & 0.049 & 0.049
\\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & 0.407 & 0.357
& 0.325 & 0.281 & 0.453 & 0.394 & 0.354 & 0.299 & 0.047 & 0.046 & 0.049 &
0.048 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& 0.475 & 0.419 & 0.387 & 0.324 & 0.529 & 0.462 & 0.420 & 0.352 & 0.047 &
0.047 & 0.049 & 0.049 \\
& \multicolumn{12}{c}{\rule{0pt}{15pt}RMSE} \\
\rule{0pt}{15pt}$\widehat{{\Greekmath 010D} }_{Z1}$ & 0.184 & 0.165 & 0.150 & 0.127 &
0.192 & 0.172 & 0.156 & 0.134 & 0.330 & 0.321 & 0.307 & 0.298 \\
\rule{0pt}{15pt}$\widehat{R}_{Z1}^{2,\textit{IdioVol-FM}}$ & 0.330 & 0.240 &
0.218 & 0.188 & 0.420 & 0.246 & 0.225 & 0.192 & 0.501 & 0.497 & 0.486 & 0.482
\\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1},C_{Z2}\right) $ & 0.409 & 0.342
& 0.307 & 0.260 & 0.500 & 0.388 & 0.345 & 0.285 & 0.322 & 0.318 & 0.319 &
0.314 \\
\rule{0pt}{15pt}$\widehat{Corr}\left( C_{Z1}^{resid},C_{Z2}^{resid}\right) $
& 0.510 & 0.399 & 0.355 & 0.287 & 0.813 & 0.481 & 0.417 & 0.323 & 0.212 &
0.207 & 0.209 & 0.205 \\
\\ \hline\hline
\end{tabular*}
{\caption{Finite sample properties of our estimators using 5 years of data sampled at 1 minute. The true values are ${\Greekmath 010D}_{Z1}=0.450$,
$R^{2,\textit{IdioVol-FM}}_{Z1}=0.35$,
$Corr \left( C_{Z1},C_{Z2} \right)=0.517$, $Corr \left( C^{resid}_{Z1},C^{resid}_{Z2} \right)=0.417$. Model 2.}}
Next, we study the
empirical rejection probabilities of the three statistical tests as outlined
in Section (ref). The first null hypothesis is the absence of
dependence between the IdioVols, $H^1_0 : [C_{Z1},C_{Z2}]_T=0$. The second
null hypothesis we test is the absence of dependence between the IdioVol of
the first stock and the market volatility, $H_0^2 : [C_{Z1},C_{X}]_T=0$. The
third null hypothesis is the absence of dependence in the two residual
IdioVols, $H_0^3 : [C_{Z1}^{resid},C_{Z2}^{resid}]_T=0$.
Table (ref) presents the empirical rejection probabilities of
the t-tests corresponding to the null hypotheses $H_{0}^{1},$ $H_{0}^{2}$,
and $H_{0}^{3}$ in the above, in Model 1. In Model 1, these null hypotheses
are true, so numbers in Table (ref) represent empirical size.
We present the results for two sampling frequencies ($\Delta _{n}=1$ minute
and $\Delta _{n}=5$ minutes) and the two type of estimators (AN and LIN). We
see that the empirical rejection probabilities are reasonably close to the
nominal size of the test.
Neither type of estimator (AN or LIN) seems to dominate the other.
Consistent with the asymptotic theory, the empirical rejection probabilities
of the three tests become closer to the nominal size of the test
when frequency is higher.
Table (ref) presents the empirical rejection probabilities of
the t-tests for the same null hypotheses in Model 2. In this model, all
three null hypotheses are false, so the numbers in the table represent
power. The magnitude of dependence between the residual IdioVols, $
[C_{Z1}^{resid},C_{Z2}^{resid}]_{T}$, is of course smaller than the
magnitude of the dependence between total IdioVols, $[C_{Z1},C_{Z2}]_{T}$,
so the power in Panel C is lower than in Panel A. However, in most of the
cases the power is still nontrivial, especially for larger block sizes $
{\Greekmath 0112} $, and clearly increasing with higher frequency.
table[table omitted — 3,089 chars of source]
table[table omitted — 3,157 chars of source]
Conclusion
We introduce an econometric framework for analysis of cross-sectional
dependence in the IdioVols of assets using high frequency data. First, we
provide bias-corrected estimators of standard measures of dependence between
IdioVols, as well as the associated asymptotic theory. Second, we study an
IdioVol Factor Model, in which we decompose the variation in IdioVols into
two parts: the variation related to the systematic factors such as the
market volatility, and the residual variation.
We provide the asymptotic theory that allows us to test, for example,
whether the residual (non-systematic) components of the IdioVols exhibit
cross-sectional dependence.
To provide the bias-corrected estimators and inference results, we develop a
new asymptotic theory for general estimators of quadratic covariation of
vector-valued (possibly) nonlinear transformations of the spot covariance
matrices. This theoretical contribution is of its own interest, and can be
applied in other contexts. For example, our results can be used to conduct
inference for the cross-sectional dependence in asset betas.
We apply our methodology to the S&P100 index components, and document
strong cross-sectional dependence in their Idiosyncratic Volatilities. We
consider two different sets of idiosyncratic volatility factors, and find
that neither can fully account for the cross-sectional dependence in
idiosyncratic volatilities. For each model, we map out the network of
dependencies in residual (non-systematic) Idiosyncratic Volatilities across
all stocks.
Acknowledgements
We are grateful to co-editors Torben Andersen and Serena Ng, two associate
editors, and four anonymous referees for numerous helpful suggestions. We
benefited from discussions with Marine Carrasco, Yoosoon Chang, Valentina
Corradi, Russell Davidson, Jean-Marie Dufour, Prosper Dovonon, Kirill
Evdokimov, S\'ilvia Gon\c calves, Peter Hansen, Jean Jacod, Dennis
Kristensen, Joon Park, Benoit Perron, and Dacheng Xiu. We thank seminar
participants at University of Amsterdam, Bank of Canada, Concordia, HEC
Montreal, Indiana, LSE, McGill, NC State, Pennsylvania, Surrey, Toulouse,
UCL, Warwick, Western Ontario, as well as participants of various
conferences, for helpful comments and suggestions.
Ilze Kalnina is grateful to UCL and CeMMaP for their hospitality and
support.
She is also grateful to the Economics Department, the Gregory C. Chow
Econometrics Research Program, and the Bendheim Center for Finance at
Princeton University for their hospitality.
\singlespacing
\FloatBarrier
appendix{ Appendix}
\setcounter{theorem}{0} \setcounter{section}{0} \setcounter{subsection}{0}
\setcounter{equation}{0}
\numberwithin{figure}{section} \numberwithin{table}{section}
\setcounter{page}{1}
Sections (ref)-(ref) contain all proofs.
Section (ref) contains some numerical implementation details.
Section (ref) contains additional figures for the empirical application.
The proofs are organised as follows.
Section (ref) introduces additional notation. Section (ref) presents auxiliary theorems and lemmas
used to prove Theorems (ref) and (ref) in the main paper.
Section (ref) proves Theorem (ref).
Section (ref) proves Theorem (ref).
Section (ref) collects the proofs of the auxiliary results of Section (ref).
\section{Notation for Proofs}
Our notation is similar to that of the proofs of jacodrosenbaum-sqrtn whenever possible. Throughout, we denote by $K$ a
generic constant, which may change from line to line.
We let by convention $\sum_{i=a}^{a^{\prime }}=0$ when $a> a^{\prime }$. For
simplicity, we omit the subscript $r$ for results involving only one object
with this subscript.
By the usual localization argument, there exists a ${\Greekmath 0119} $
-integrable function $J$ on $E$ and a constant such that the stochastic
processes in equations ((ref)) and ((ref)) satisfy
\begin{equation}
\Vert b\Vert ,\Vert \widetilde{b}\Vert ,\Vert c\Vert ,\Vert \widetilde{c}
\Vert ,J\leq A,\Vert {\Greekmath 010E} (w,t,z)\Vert ^{r}\leq J(z).
\end{equation}
We set
\begin{equation*}
\mathcal{F}_{i}^{n}=\mathcal{F}_{i \Delta _{n}}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{, }
C_{i}^{n}=C_{i\Delta _{n}}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{, }
\overline{C}_{i}^{n}=\overline{C}_{i\Delta _{n}}
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{, and }
\widehat{C}_{i}^{n}=\widehat{C}_{i\Delta _{n}}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{.}
\end{equation*}
For any c\`{a}dl\`{a}g bounded process $Z$, we set
\begin{align*}
& {\Greekmath 0111} _{t,s}(Z)=\sqrt{\mathbb{E}\Big(\sup_{0<u\leq s}\Vert
Z_{t+u}-Z_{t}\Vert ^{2}|\mathcal{F}_{t}\Big)}, \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and} \\
& {\Greekmath 0111} _{i,j}^{n}(Z)=\sqrt{\mathbb{E}\Big(\sup_{0\leq u\leq j\Delta
_{n}}\Vert Z_{(i-1)\Delta _{n}+u}-Z_{(i-1)\Delta _{n}}\Vert ^{2}|\mathcal{F}
_{(i-1)\Delta _{n}}\Big)}.
\end{align*}
For convenience, we decompose $Y_{t}$ as
\begin{equation*}
Y_{t}=Y_{0}+Y_{t}^{\prime }+\sum_{s\leq t}\Delta Y_{s}.
\end{equation*}
where $Y_{t}^{\prime }=\int_{0}^{t}b_{s}^{^{\prime }}ds+\int_{0}^{t}{\Greekmath 011B}
_{s}dW_{s}$ and $b_{t}^{\prime }=b_{t}-\int {\Greekmath 010E} (t,z)1_{\{\Vert {\Greekmath 010E}
(t,z)\Vert \leq 1\}}{\Greekmath 0119} (dz)$.\newline
Let $\widehat{C}_{i}^{\prime n}$ be the local estimator of the spot variance
of the unobservable process $Y^{\prime }$, i.e.,
\begin{equation}
\widehat{C}_{i}^{\prime n}=\frac{1}{k_{n}\Delta _{n}}\sum_{u=0}^{k_{n}-1}(
\Delta _{i+u}^{n}Y^{\prime })(\Delta _{i+u}^{n}Y)^{\prime \top }=(\widehat{C}
_{i}^{\prime n,gh})_{1\leq g,h\leq d}.
\end{equation}
There is no price jump truncation applied in the definition of $\widehat{C}
_{i}^{\prime n}$ since the process $Y^{\prime }$ is continuous. Hence, it is
more convenient to work with $\widehat{C}_{i}^{\prime n}$ rather than $
\widehat{C}_{i}^{n}$ ($=\widehat{C}_{i\Delta _{n}}$, defined in equation ((ref))).
We also define
\begin{equation}
{\Greekmath 010B} _{i}^{n}
=(\Delta _{i}^{n}Y^{\prime })(\Delta _{i}^{n}Y^{\prime})^{\top }
-
C_{(i-1)\Delta _{n}}\Delta _{n},
{\Greekmath 0117} _{i}^{n}
=\widehat{C}_{i}^{^{\prime }n}-C_{(i-1)\Delta _n},
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}
{\Greekmath 0115}_{i}^{n}
=\widehat{C}_{i+k_{n}}^{^{\prime }n}-\widehat{C}_{i}^{^{\prime }n},
\end{equation}
which satisfy
\begin{equation}
{\Greekmath 0117} _{i}^{n}
=
\frac{1}{k_{n}\Delta _{n}}\sum_{j=0}^{k_{n}-1}({\Greekmath 010B}_{i+j}^{n}
+(C_{(i+j-1)\Delta _n}-C_{(i-1)\Delta _n})\Delta _{n})
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}
{\Greekmath 0115} _{i}^{n}
=
{\Greekmath 0117} _{i+k_{n}}-{\Greekmath 0117} _{i}^{n}
+C_{(i+k_n-1)\Delta _n}-C_{(i-1)\Delta _n}.
\end{equation}
The following multidimensional quantities will be used in the
sequel
\begin{center}
\begin{tabular}{ll}
${\Greekmath 0110} (1)_{i}^{n}=\frac{1}{\Delta _{n}}\Delta _{i}^{n}Y^{\prime }(\Delta
_{i}^{n}Y^{\prime })^{\top }-C_{i-1}^{n},$ & ${\Greekmath 0110} (2)_{i}^{n}=\Delta
_{i}^{n}c,$ \\
${\Greekmath 0110} ^{\prime }(u)_{i}^{n}=\mathbb{E}({\Greekmath 0110} (u)_{i}^{n}|\mathcal{F}
_{i-1}^{n}),$ & ${\Greekmath 0110} ^{\prime \prime }(u)_{i}^{n}={\Greekmath 0110} (u)_{i}^{n}-{\Greekmath 0110}
^{\prime }(u)_{i}^{n},$ \\
${\Greekmath 0110} ^{r}(u)_{i}^{n}=\Big({\Greekmath 0110} ^{r}(u)_{i}^{n,gh}\Big)_{1\leq g,h\leq d}$
& $\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{with }r=^{\prime }\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ or }^{\prime \prime }.$
\end{tabular}
\end{center}
For $1\leq g,h\leq d$ and $u,v=1,2$, define
\begin{equation*}
{\Greekmath 011A} _{gh}(u,v)_{i}^{n}=\sum_{m=1}^{2k_{n}-1}{\Greekmath 0115} (u,v)_{m}^{n}{\Greekmath 0110}
_{gh}(u)_{i-m}^{n}.
\end{equation*}
We also define, for $m\in \{0,\ldots ,2k_{n}-1\}$ and $j,l\in
\mathbb{Z}$,
\begin{equation*}
{\Greekmath 0122} (1)_{m}^{n}=
\begin{cases}
-1 & if0\leq m<k_{n} \\
+1 & ifk_{n}\leq m<2k_{n},
\end{cases}
,\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{\Greekmath 0122} (2)_{m}^{n}=\sum_{q=m+1}^{2k_{n}-1}{\Greekmath 0122}
(1)_{q}^{n}=(m+1)\wedge (2k_{n}-m-1),
\end{equation*}
For any $u,v,m,u^{\prime },v^{\prime }$, we set
\begin{equation*}
z_{u,v}^{n}=
\begin{cases}
1/\Delta _{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}u=v=1 \\
1 & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{otherwise},
\end{cases}
\end{equation*}
\begin{align*}
{\Greekmath 0115} (u,v;m)_{j,l}^{n}& =\frac{3}{2k_{n}^{3}}\sum_{q=0\vee
(j-m)}^{(l-m-1)\vee (2k_{n}-m-1)}{\Greekmath 0122} (u)_{q}^{n}{\Greekmath 0122}
(u)_{q+m}^{n},{\Greekmath 0115} (u,v)_{m}^{n}={\Greekmath 0115}
(u,v;m)_{0,2k_{n}}^{n}, \\
M(u,v;u^{\prime },v^{\prime })_{n}& =z_{u,v}^{n}z_{u^{\prime },v^{\prime
}}^{n}\sum_{m=1}^{2k_{n}-1}{\Greekmath 0115} (u,v)_{m}^{n}{\Greekmath 0115} (u^{\prime
},v^{\prime })_{m}^{n}.
\end{align*}
We also need some notation for volatility jumps. Denote by $N_{s}$
the number of jumps in $C$ from time $0$ to $s$. Let
\begin{eqnarray}
L\left( n\right) &=&\left\{ i=k_{n}+1,k_{n}+2,...:N_{\left( i+3\right)
k_{n}\Delta _{n}}-N_{\left( i-1\right) k_{n}\Delta _{n}}=0\right\} , \notag
\\
L\left( n,T\right) &=&\left\{ i=1,2,...,\left[ T/\Delta _{n}\right]
-3k_{n}+1\right\} \cap L\left( n\right) , \\
L^{\prime }\left( n,T\right) &=&\left\{ i=1,2,...,\left[ T/\Delta _{n}\right]
:i-2k_{n}\in L\left( n,T\right) \right\} , \notag \\
\overline{L}\left( n,T\right) &=&\left\{ i=1,2,...,\left[ T/\Delta _{n}
\right] -3k_{n}+1\right\} \backslash L\left( n\right) . \notag
\end{eqnarray}
Additionally, set
\begin{align}
\overline{A11}(H,gh,u;G,ab,v)_{T}^{n}&
=
\frac{3}{2k_{n}^{3}}\sum_{i\in
L^{\prime }\left( n,T\right) }\Big(\sum_{j=0}^{2k_{n}-1}{\Greekmath 0122}
(u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}\Big)(\partial _{gh}H\partial
_{ab}G)(C_{(i-2k_n-1)\Delta_n}){\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab} \notag \\
&
=
{\Greekmath 0115} (u,v)_{0}^{n}\sum_{i\in L^{\prime }\left( n,T\right) }(\partial
_{gh}H\partial _{ab}G)(C_{(i-2k_n-1)\Delta_n}){\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110}
(v)_{i}^{n,ab},
\end{align}
and
\begin{align}
\overline{A12}(H,gh,u;G,ab,v)_{T}^{n}& =\frac{3}{2k_{n}^{3}}\sum_{i\in
L^{\prime }\left( n,T\right) }(\partial _{gh}H\partial
_{ab}G)(C_{(i-2k_n-1)\Delta_n})\sum_{m=1}^{(i-1)\wedge
(2k_{n}-1)}\sum_{j=0}^{(2k_{n}-m-1)}{\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122}
(v)_{j+m}^{n} \notag \\
& \times {\Greekmath 0110} _{gh}(u)_{i-m}^{n}{\Greekmath 0110} _{ab}(v)_{i}^{n}.
\end{align}
Denote by ${\Greekmath 0123} _{i}^{AN}$ and ${\Greekmath 0123} _{i}^{LIN}$ the $i^{th}$
summand of $\widehat{\left[ H(C),G(C)\right] _{T}^{c}}^{AN}$ and $\widehat{
\left[ H(C),G(C)\right] _{T}^{c}}^{LIN}$, without the volatility jump
truncation, so they satisfy
\begin{eqnarray}
\widehat{\left[ H(C),G(C)\right] _{T}^{c}}^{AN}
&=&\sum_{i=k_{n}+1}^{[T/\Delta _{n}]-3k_{n}+1}{\Greekmath 0123} _{i}^{AN}1_{\left\{
A_{i}\cap A_{i+k_{n}}\right\} },\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ and} \\
\widehat{\left[ H(C),G(C)\right] _{T}^{c}}^{LIN}
&=&\sum_{i=k_{n}+1}^{[T/\Delta _{n}]-3k_{n}+1}{\Greekmath 0123} _{i}^{LIN}1_{\left\{
A_{i}\cap A_{i+k_{n}}\right\} }
\end{eqnarray}
Let ${\Greekmath 0123} _{i}$ be either ${\Greekmath 0123} _{i}^{LIN}$ or ${\Greekmath 0123}
_{i}^{AN} $.
\section{Auxiliary Lemmas and Theorems}
This section presents useful auxiliary results,
which are used in the proofs of Theorems (ref) and (ref).
The results of this section are proved in Section (ref)
below.
First, we explain why we can assume, without loss of generality, that the
derivatives of functions $H_{r}$ and $G_{r}$ are bounded, for $r=1,\ldots
,{\Greekmath 0114} $. Assumptions of Theorem (ref) imply Lemma 2 of litodorovtauchen17-adaptive. Therefore, we can assume that the variables $
\widehat{C}_{i\Delta _{n}}$ are bounded, uniformly over $i\in \left\{ 0,...,
\left[ T/\Delta _{n}\right] -k_{n}+1\right\} $, with probability approaching
one. Using the spatial localization argument of litodorovtauchen-dependencies, which in turn uses the spatial localization
argument of litodorovtauchen17-adaptive, we can assume that $H_{r}$
and $G_{r}$ are compactly supported without loss of generality. Hence, the
derivatives of functions $H_{r}$ and $G_{r}$ are bounded, for $r=1,\ldots
,{\Greekmath 0114} $.
We start with two auxiliary theorems for volatility jump truncation.
\begin{theorem}
Under the assumptions of Theorem (ref),
we have
\begin{equation}
\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap
A_{i+k_{n}}\right\} }-\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}=o_{p}\left( \Delta _{n}^{1/4}\right) . \notag
\end{equation}
\end{theorem}
\begin{theorem}
Under the assumptions of Theorem (ref), we have
\begin{equation}
\sum_{i\in \overline{L}\left( n,T\right) }{\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap
A_{i+k_{n}}\right\} }=o_{p}\left( \Delta _{n}^{1/4}\right) . \notag
\end{equation}
\end{theorem}
Theorems (ref) and (ref) allow us
to focus on the simpler leading term $\sum_{i\in L\left( n,T\right)
}{\Greekmath 0123} _{i}$ instead of the original estimator(s) $\sum_{i=k_{n}+1}^{[T/
\Delta _{n}]-3k_{n}+1}{\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\}
}$ for the remaining proofs. Our next theorem shows negligibility of price
jump truncation.
\begin{theorem}
Let ${\Greekmath 0123} _{i}^{\prime LIN}$ and ${\Greekmath 0123}
_{i}^{\prime AN}$ be the modifications of ${\Greekmath 0123} _{i}^{LIN}$ and $
{\Greekmath 0123} _{i}^{AN}$ obtained by replacing $\widehat{C}_{i}^{n}$ by $
\widehat{C}_{i}^{^{\prime }n}$ in the definition of ${\Greekmath 0123} _{i}^{LIN}$
and ${\Greekmath 0123} _{i}^{AN}$ in equations ((ref)) and ((ref)). Under the assumptions of Theorem (ref), we have
\begin{align}
\Delta _{n}^{-1/4}\Big(\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}^{LIN}-\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\prime LIN}\Big)&
\overset{\mathbb{P}}{\longrightarrow }0 \notag \\
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}\Delta _{n}^{-1/4}\Big(\sum_{i\in L\left( n,T\right)
}{\Greekmath 0123} _{i}^{AN}-\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\prime
AN}\Big)& \overset{\mathbb{P}}{\longrightarrow }0.
\end{align}
\end{theorem}
Theorem (ref) allows, in particular, to focus on
the derivation of the asymptotic distributions of $\sum_{i\in L\left(
n,T\right) }{\Greekmath 0123} _{i}^{\prime LIN}$ and $\sum_{i\in L\left( n,T\right)
}{\Greekmath 0123} _{i}^{\prime AN}$. The next theorem connects the LIN and
AN versions of these quantities. To state the theorem, define
\begin{align}
{\Greekmath 0123} _{i}^{\left( A\right) }& =\frac{3}{2k_{n}}\sum_{g,h,a,b=1}^{d}
\Bigg(\Big(\partial _{gh}H\partial _{ab}G\big)
(C_{(i-1)\Delta_n})\Big[(\widehat{C}
_{i+k_{n}}^{^{\prime }n,gh}-\widehat{C}_{i}^{^{\prime }n,gh})(\widehat{C}
_{i+k_{n}}^{^{\prime }n,ab}-\widehat{C}_{i}^{^{\prime }n,ab})
\\
& -\frac{2}{k_{n}}(\widehat{C}_{i}^{^{\prime }n,ga}\widehat{C}_{i}^{^{\prime
}n,hb}+\widehat{C}_{i}^{^{\prime }n,gb}\widehat{C}_{i}^{^{\prime }n,ha})\Big]
\Bigg). \notag
\end{align}
where
superscript $\left( A\right) $ stands for \textquotedblleft approximated".
For simplicity, we do not index the above quantity by a prime although it
depends on $\widehat{C}_{i}^{^{\prime }n}$ instead of $\widehat{C}_{i}^{n}$.
\begin{theorem}
Under the assumptions of Theorem (ref), we
have
\begin{align}
& \Delta _{n}^{-1/4}\Big(\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}^{\prime LIN}-\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\left(
A\right) }\Big)\overset{\mathbb{P}}{\longrightarrow }0\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}
\notag \\
& \Delta _{n}^{-1/4}\Big(\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}^{\prime AN}-\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\left(
A\right) }\Big)\overset{\mathbb{P}}{\longrightarrow }0,
\end{align}
where ${\Greekmath 0123} _{i}^{\left( A\right) }$ is defined in equation ((ref)).
\end{theorem}
Theorem (ref) shows that the leading terms of
the the two estimators of $\widehat{[H(C),G(C)]}_{T}^{c}$, $\sum_{i\in
L\left( n,T\right) }{\Greekmath 0123} _{i}^{\prime LIN}$ and $\sum_{i\in L\left(
n,T\right) }{\Greekmath 0123} _{i}^{\prime AN}$ can be approximated by a certain
quantity with an error of approximation of order smaller than $\Delta
_{n}^{-1/4}$.
Now, we decompose the approximated estimator as follows
\begin{equation}
{\Greekmath 0123} _{i}^{\left( A\right) }={\Greekmath 0123} _{i}^{\left( A1\right)
}-{\Greekmath 0123} _{i}^{\left( A2\right) },
\end{equation}
with
\begin{equation*}
{\Greekmath 0123} _{i}^{\left( A1\right) }=\frac{3}{2k_{n}}\sum_{g,h,a,b=1}^{d}\big(
\partial _{gh}H\partial _{ab}G\big)(C_{i-1}^{n})(\widehat{C}
_{i+k_{n}}^{^{\prime }n,gh}-\widehat{C}_{i}^{^{\prime }n,gh})(\widehat{C}
_{i+k_{n}}^{^{\prime }n,ab}-\widehat{C}_{i}^{^{\prime }n,ab}),
\end{equation*}
and
\begin{equation*}
{\Greekmath 0123} _{i}^{\left( A2\right) }=\frac{3}{k_{n}^{2}}\sum_{g,h,a,b=1}^{d}
\big(\partial _{gh}H\partial _{ab}G\big)(C_{i-1}^{n})(\widehat{C}
_{i}^{^{\prime }n,ga}\widehat{C}_{i}^{^{\prime }n,hb}+\widehat{C}
_{i}^{^{\prime }n,gb}\widehat{C}_{i}^{^{\prime }n,ha}).
\end{equation*}
The following theorem holds:
\begin{theorem}
Under the assumptions of Theorem (ref), we
have
\begin{align*}
& \frac{1}{\Delta _{n}^{1/4}}\Bigg(\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}^{\left( A1\right) }-\sum_{g,h,a,b=1}^{d}\sum_{u,v=1}^{2}\overline{A11}
(H,gh,u;G,ab,v)_{T}^{n}+\overline{A12}(H,gh,u;G,ab,v)_{T}^{n} \\
& +\overline{A12}(G,ab,v;H,gh,u)_{T}^{n}\Bigg)\overset{\mathbb{P
}}{\Longrightarrow }0.
\end{align*}
\end{theorem}
\begin{lemma}
For any c\`{a}dl\`{a}g bounded process $Z$, for all $
t,s>0 $, $j,k\geq 0$, set ${\Greekmath 0111}_{t,s}={\Greekmath 0111}_{t,s}(Z)$. Then,
\begin{align*}
&\Delta_n \mathbb{E}\Bigg(\sum_{i=1}^{[t/\Delta_n]} {\Greekmath 0111}_{i,k_n}\Bigg)
\longrightarrow 0,\Delta_n \mathbb{E}\Bigg(\sum_{i=1}^{[t/
\Delta_n]} {\Greekmath 0111}_{i,2k_n}\Bigg)\longrightarrow 0, \\
& \mathbb{E}\Bigg({\Greekmath 0111}_{i+j,k}|\mathcal{F}_i^n\Bigg)\leq {\Greekmath 0111}_{i,j+k}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}\Delta_n \mathbb{E}\Bigg(\sum_{i=1}^{[t/\Delta_n]}
{\Greekmath 0111}_{i,4k_n}\Bigg)\longrightarrow 0.
\end{align*}
\end{lemma}
\begin{lemma}
Let $Z$ be a continuous It\^o process with drift $b_t^Z$ and spot
variance process $C_t^{Z}$, and set ${\Greekmath 0111}_{t,s}={\Greekmath 0111}_{t,s}(b^Z,c^Z)$. Then,
the following bounds hold:
\begin{align}
&\Big|\mathbb{E}(Z_t\Big|\mathcal{F}_0)-tb_0^Z\Big|\leq Kt{\Greekmath 0111}_{0,t} \notag
\\
&\Big|\mathbb{E}(Z_t^{j}Z_t^{k}-tC_0^{Z,jk}\Big|\mathcal{F}_0)\Big|\leq
Kt^{3/2}(\sqrt{\Delta_n}+{\Greekmath 0111}_{0,t}) \notag \\
&\Big|\mathbb{E}\big((Z_t^{j}Z_t^{k}-tC_0^{Z,jk})(C_t^{Z,lm}-C_0^{Z,lm})\Big|
\mathcal{F}_0\big)\Big|\leq Kt^2 \notag \\
&\Big|\mathbb{E}(Z_t^{j}Z_t^{k}Z_t^{l}Z_t^{m}\Big|\mathcal{F}
_0)-
\Delta_n^2(C_0^{Z,jk}C_0^{Z,lm}+C_0^{Z,jl}C_0^{Z,km}+C_0^{Z,jm}C_0^{Z,kl})
\Big|\leq Kt^{5/2} \notag \\
&\Big|\mathbb{E}(Z_t^{j}Z_t^{k}Z_t^{l}\Big|\mathcal{F}_0)\Big|\leq Kt^2
\notag \\
&\Big|\mathbb{E}(\prod_{l=1}^6 Z_t^{j_l}\Big|\mathcal{F}_0)-\frac{\Delta_n^3
}{6}\sum_{l< l^{\prime }}\sum_{k< k^{\prime }}\sum_{m< m^{\prime
}}C_0^{Z,j_lj_{l^{\prime }}}C_0^{Z,j_kj_{k^{\prime }}}C_0^{Z,j_mj_{m^{\prime
}}}\Big|\leq Kt^{7/2} \notag \\
&\mathbb{E}\Big(\sup_{w\in[0,s]}\Big\|Z_{t+w}-Z_t\Big\|^q \Big|\mathcal{F}_t
\Big) \leq K_q s^{q/2},\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}\Big\|\mathbb{E}
\Big(Z_{t+s}-Z_t\Big)\Big|\mathcal{F}_t\Big\|\leq Ks. \\
\end{align}
\end{lemma}
\begin{lemma}
Let ${\Greekmath 0110}_i^n$ be a $r$-dimensional $\mathcal{F}_i^n $
-measurable process satisfying $\|\mathbb{E}({\Greekmath 0110}_{i}^n|\mathcal{F}
_{i-1}^n)\| \leq L^{\prime }$ and $\mathbb{E}\Big(\|{\Greekmath 0110}_{i}^n\|^q \Big|
\mathcal{F}_{i-1}^n\Big) \leq L_q $. Also, let ${\Greekmath 0127}_i^n$ be a
real-valued $\mathcal{F}_{i}^n$-measurable process with $\mathbb{E}\Big(
\|{\Greekmath 0127}_{i+j-1}^n\|^q \Big|\mathcal{F}_{i-1}^n\Big) \leq L^q $ for $q \geq
2$ and $1\leq j \leq 2k_n-1$. Then,
\begin{align*}
\mathbb{E}\Bigg(\Bigg\|\sum_{j=1}^{2k_n-1}{\Greekmath 0127}_{i+j-1}^n{\Greekmath 0110}_{i+j}^n
\Bigg\|^q \Bigg|\mathcal{F}_{i-1}^n\Bigg)\leq K_qL^q\Big(L_qk_n^{q/2}+L^{
\prime q}k_n^q\Big).
\end{align*}
\end{lemma}
\begin{lemma}
Under the assumptions of Theorem (ref), we have, for $i\in L\left(
n,T\right) $:
\begin{align*}
& \Bigg|\mathbb{E}\Big(\left. {\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}{\Greekmath 0115}
_{i+2k_{n}}^{n,gh}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}\right
\vert
\mathcal{F}_{i-1}^{n}
\Big)-\frac{4}{k_{n}^{2}}\Big(
C_{i-1}^{n,ga}C_{i-1}^{n,hb}+C_{i-1}^{n,gb}C_{i-1}^{n,ha})(C_{i-1}^{n,jl}C_{i-1}^{n,km}+C_{i-1}^{n,jm}C_{i-1}^{n,kl}
\Big) \\
& -\frac{4\Delta _{n}}{3}\Big(
C_{i-1}^{n,jl}C_{i-1}^{n,km}+C_{i-1}^{n,jm}C_{i-1}^{n,kl}\Big)\overline{C}
_{i-1}^{n,gh,ab}-\frac{4\Delta _{n}}{3}\Big(
C_{i-1}^{n,ga}C_{i-1}^{n,hb}-C_{i-1}^{n,gb}C_{i-1}^{n,ha}\Big)\overline{C}
_{i-1}^{n,jk,lm} \\
& -\frac{4(k_{n}\Delta _{n})^{2}}{9}\overline{C}_{i-1}^{n,gh,ab}\overline{C}
_{i-1}^{n,jk,lm}\Bigg|\leq K\Delta _{n}(\Delta _{n}^{1/8}+{\Greekmath 0111} _{i,4k_{n}}^{n}
\Big).
\end{align*}
\end{lemma}
\begin{lemma}
Under the assumptions of Theorem (ref), we have, for $i\in L\left(
n,T\right) $:
\begin{align}
& & \Big|\mathbb{E}\Big(\left. {\Greekmath 0117} _{i}^{n,jk}{\Greekmath 0117} _{i}^{n,lm}{\Greekmath 0117}
_{i}^{n,gh}\right\vert \mathcal{F}_{i-1}^{n}\Big)\Big|& \leq K\Delta _{n}^{3/4}
\Big(\Delta _{n}^{1/4}+{\Greekmath 0111} _{i,k_{n}}^{n}\Big),
\\
& & \Big|\mathbb{E}\Big({\Greekmath 0117} _{i}^{n,jk}{\Greekmath 0117} _{i}^{n,lm}\left. \Big(
C_{i+k_{n}-1}^{n,gh}-C_{i-1}^{n,gh}\Big)\right\vert \mathcal{F}_{i-1}^{n}\Big)\Big|
& \leq K\Delta _{n}^{3/4}\Big(\Delta _{n}^{1/4}+{\Greekmath 0111} _{i,k_{n}}^{n}\Big),
\\
& & \Big|\mathbb{E}\Big({\Greekmath 0117} _{i}^{n,jk}\Big(C_{i+k_{n}-1}^{n,lm}-C_{i-1}^{n,lm}
\Big)\left. \Big(C_{i+k_{n}-1}^{n,gh}-C_{i-1}^{n,gh}\Big)\right\vert \mathcal{F}
_{i-1}^{n}\Big)\Big|& \leq K\Delta _{n}^{3/4}\Big(\Delta _{n}^{1/4}+{\Greekmath 0111}
_{i,k_{n}}^{n}\Big),
\\
& & \Big|\mathbb{E}\Big(\left. {\Greekmath 0117} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}{\Greekmath 0115}
_{i}^{n,gh}\right\vert \mathcal{F}_{i-1}^{n}\Big)\Big|& \leq K\Delta _{n}^{3/4}
\Big(\Delta _{n}^{1/4}+{\Greekmath 0111} _{i,2k_{n}}^{n}\Big),
\\
& & \Big|\mathbb{E}\Big(\left. {\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}{\Greekmath 0115}
_{i}^{n,gh}\right\vert \mathcal{F}_{i-1}^{n}\Big)\Big|& \leq K\Delta _{n}^{3/4}
\Big(\Delta _{n}^{1/4}+{\Greekmath 0111} _{i,2k_{n}}^{n}\Big).
\end{align}
\end{lemma}
\begin{lemma}
Under the assumptions of Theorem (ref), we have:
\begin{align}
& \frac{1}{\Delta _{n}^{1/4}}\sum_{i\in L\left( n,T\right) }(\partial
_{gh}H\partial _{ab}G)(C_{(i-2k_{n}-1)\Delta_n}){\Greekmath 011A} _{gh}(u,v)_{i}^{n}{\Greekmath 0110}
_{ab}^{^{\prime }}(v)_{i}^{n}\overset{\mathbb{P}}{\Longrightarrow }
0, \forall (u,v) \\
& \frac{1}{\Delta _{n}^{1/4}}\Big(\overline{A11}(H,gh,u;G,ab,v)-
\int_{0}^{T}(\partial _{gh}H\partial _{ab}G)(C_{t})\overline{C}_{t}^{gh,ab}dt
\Big)\overset{\mathbb{P}}{\Longrightarrow }0\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{when}
(u,v)=(2,2) \\
& \frac{1}{\Delta _{n}^{1/4}}\Big(\overline{A11}(H,gh,u;G,ab,v)-\frac{3}{
{\Greekmath 0112} ^{2}}\int_{0}^{T}(\partial _{gh}H\partial
_{ab}G)(C_{t})(C_{t}^{ga}C_{t}^{hb}+C_{t}^{gb}C_{t}^{ha})dt\Big)\overset{
\mathbb{P}}{\Longrightarrow }0 \\
& \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{when} (u,v)=(1,1), \notag \\
& \frac{1}{\Delta _{n}^{1/4}}\overline{A11}(H,gh,u;G,ab,v)\overset{\mathbb{P}
}{\Longrightarrow }0\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{when} (u,v)=(1,2),(2,1)
\end{align}
\end{lemma}
\section{Proof of Theorem (ref)}
We now prove Theorem (ref). By Theorem (ref), we
have
\begin{align*}
\frac{1}{\Delta _{n}^{1/4}}\Bigg(\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}^{\left( A1\right) }& -\sum_{g,h,a,b=1}^{d}\sum_{u,v=1}^{2}\overline{A11}
(H,gh,u;G,ab,v)_{T}^{n}+\overline{A12}(H,gh,u;G,ab,v)_{T}^{n} \\
& +\overline{A12}(G,ab,v;H,gh,u)_{T}^{n}\Bigg)\overset{\mathbb{P}}{
\Longrightarrow }0.
\end{align*}
Recalling the definition of $\overline{A12}(H,gh,u;G,ab,v)_{T}^{n}$ from
equation ((ref)), Lemma (ref) implies that
\begin{align}
& \frac{1}{\Delta _{n}^{1/4}}\Bigg(\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}^{\left( A\right) }-[H(C),G(C)]_{T}-\frac{3}{2k_{n}^{3}}
\sum_{g,h,a,b}^{d}\sum_{u,v=1}^{2}\sum_{i\in L^{\prime }\left( n,T\right) }
\\
& \Big[(\partial _{gh}H\partial _{ab}G)(C_{(i-2k_{n}-1)\Delta_n}){\Greekmath 011A}
_{gh}(u,v)_{i}^{n}{\Greekmath 0110} _{ab}^{^{\prime \prime }}(v)_{i}^{n}+(\partial
_{ab}H\partial _{gh}G)(C_{(i-2k_{n}-1)\Delta_n}){\Greekmath 011A} _{ab}(v,u)_{i}^{n}{\Greekmath 0110}
_{gh}^{^{\prime \prime }}(v)_{i}^{n}\Big]\Bigg)\overset{\mathbb{P}}{
\Longrightarrow }0. \notag
\end{align}
Next, define
\begin{align*}
& {\Greekmath 0118} (H,gh,u;G,ab,v)_{i}^{n}=\frac{1}{\Delta _{n}^{1/4}}(\partial
_{gh}H\partial _{ab}G)(C_{(i-2k_{n}-1)\Delta_n}){\Greekmath 011A} _{gh}(u,v)_{i}^{n}{\Greekmath 0110}
_{ab}^{\prime \prime }(v)_{i}^{n}, \\
& Z(H,gh,u;G,ab,v)_{t}^{n}=\Delta _{n}^{1/4}\sum_{i=2k_{n}}^{[t/\Delta
_{n}]}{\Greekmath 0118} (H,gh,u;G,ab,v)_{i}^{n}.
\end{align*}
Notice that ((ref)) implies
\begin{align}
& \frac{1}{\Delta _{n}^{1/4}}\Big(\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}^{\left( A\right) }-[H(C),G(C)]_{T}\Big)\overset{\mathcal{L}}{=}
\sum_{g,h,a,b=1}^{d}\sum_{u,v=1}^{2}\frac{1}{\Delta _{n}^{1/4}}\Big(
Z(H,gh,u;G,ab,v)_{T}^{n} \notag \\
& +Z(H,ab,v;G,gh,u)_{T}^{n}\Big).
\end{align}
The term ${\Greekmath 0123} _{i}^{\left( A\right) }$ depends on functions $H$ and $G$
, where we have so far suppressed the subscripts $r$, $r=1,..,{\Greekmath 0114} $, in
the statement of Theorem (ref) for simplicity. Denote by ${\Greekmath 0123}
_{i,r}^{\left( A\right) }$ the term ${\Greekmath 0123} _{i}^{\left( A\right) }$ that
depends on functions $H_{r}$ and $G_{r}$. Observe that to derive the
asymptotic distribution of $\left( \sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i,1}^{\left( A\right) },...,\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i,{\Greekmath 0114} }^{\left( A\right) }\right) $ , it suffices to study the joint asymptotic
behavior of the family of processes $\frac{1}{\Delta _{n}^{1/4}}
Z(H,gh,u;G,ab,v)_{T}^{n}$. Notice that ${\Greekmath 0118} (H,gh,u;G,ab,v)_{i}^{n}$ are
martingale increments relative to the discrete filtration $(\mathcal{F}
_{i}^{n})$. Therefore, to obtain the joint asymptotic distribution of $\frac{
1}{\Delta _{n}^{1/4}}Z(H,gh,u;G,ab,v)_{T}^{n}$, it is enough to prove the
following three properties:
\begin{align}
& A\Big((H,gh,u;G,ab,v),(H^{\prime },g^{\prime }h^{\prime },u^{\prime
};G^{\prime },a^{\prime }b^{\prime },v^{\prime })\Big)_{t}^{n} \notag \\
& =\sum_{i\in L^{\prime }\left( n,T\right) }\mathbb{E}({\Greekmath 0118}
(H,gh,u;G,ab,v)_{i}^{n}{\Greekmath 0118} (H^{\prime },g^{\prime }h^{\prime },u^{\prime
};G^{\prime },a^{\prime }b^{\prime },v^{\prime })_{i}^{n}|\mathcal{F}
_{i-1}^{n}) \\
& \overset{\mathbb{P}}{\Longrightarrow }A\Big(
(H,gh,u;G,ab,v),(H^{\prime },g^{\prime }h^{\prime },u^{\prime };G^{\prime
},a^{\prime }b^{\prime },v^{\prime })\Big)_{t}, \\
& \sum_{i\in L^{\prime }\left( n,T\right) }\mathbb{E}\Big(\Big|{\Greekmath 0118}
(H,gh,u;G,ab,v\Big)_{i}^{n}\Big|^{4}\Big|\mathcal{F}_{i-1}^{n})\overset{
\mathbb{P}}{\Longrightarrow }0,\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ and} \\
& B(N;H,gh,u;G,ab,v)_{t}^{n}:=\sum_{i\in L^{\prime }\left( n,T\right) }
\mathbb{E}\Big({\Greekmath 0118} (H,gh,u;G,ab,v)_{i}^{n}\Delta _{i}^{n}N|\mathcal{F}
_{i-1}^{n}\Big)\overset{\mathbb{P}}{\Longrightarrow }0,
\end{align}
for all $t>0$, all $(H,gh,u;G,ab,v),(H^{\prime },g^{\prime }h^{\prime
},u^{\prime };G^{\prime },a^{\prime }b^{\prime },v^{\prime })$ and all
martingales $N$ which are either bounded and orthogonal to $W$, or equal to
one component $W^{j}$.\newline
Since the derivatives of $H_{r}$ and $G_{r}$ are bounded, equations ((ref)) and ((ref)) can be proved by an extension of (B.105)
and (B.106) in yacjacod14 to multivariate processes.\newline
Next, define
\begin{equation*}
V_{ab}^{a^{\prime }b^{\prime }}(v,v^{\prime })_{t}=
\begin{cases}
(C_{t}^{aa^{\prime }}C_{t}^{bb^{\prime }}+C_{t}^{ab^{\prime
}}C_{t}^{ba^{\prime }}) & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(v,v^{\prime })=(1,1) \\
\overline{C}_{t}^{ab,a^{\prime }b^{\prime }} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}
(v,v^{\prime })=(2,2) \\
0 & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{otherwise}.
\end{cases}
\end{equation*}
Using again the boundedness of the derivatives of $H_{r}$ and $G_{r}$, we
can show that
\begin{align*}
A\Big((H,gh,u;G,ab,v),& (H^{\prime },g^{\prime }h^{\prime },u^{\prime
};G^{\prime },a^{\prime }b^{\prime },v^{\prime })\Big)_{t}= \\
& M(u,v;u^{\prime },v^{\prime })\int_{0}^{t}(\partial _{gh}H\partial
_{ab}G\partial _{g^{\prime }h^{\prime }}H\partial _{a^{\prime }b^{\prime
}}G)(C_{s})V_{ab}^{a^{\prime }b^{\prime }}(v,v^{\prime })_{s}V
_{gh}^{g^{\prime }h^{\prime }}(u,u^{\prime })_{s}ds,
\end{align*}
with
\begin{equation*}
M(u,v;u^{\prime },v^{\prime })=
\begin{cases}
3/{\Greekmath 0112} ^{3} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v;u^{\prime },v^{\prime })=(1,1;1,1)
\\
3/4{\Greekmath 0112} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v;u^{\prime },v^{\prime
})=(1,2;1,2),(2,1;2,1) \\
151{\Greekmath 0112} /280 & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v;u^{\prime },v^{\prime })=(2,2;2,2)
\\
0 & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{otherwise}.
\end{cases}
\end{equation*}
Therefore, we have \newline
$A\Big((H,gh,u;G,ab,v),(H^{\prime },g^{\prime }h^{\prime },u^{\prime
};G^{\prime },a^{\prime }b^{\prime },v^{\prime })\Big)_{T}$ =
\begin{equation*}
\begin{cases}
\frac{3}{{\Greekmath 0112} ^{3}}\int_{0}^{T}(\partial _{gh}H\partial _{ab}G\partial
_{g^{\prime }h^{\prime }}H^{\prime }\partial _{a^{\prime }b^{\prime
}}G^{\prime })(C_{t})(C_{t}^{gg^{\prime }}C_{t}^{hh^{\prime
}}+C_{t}^{gh^{\prime }}C_{t}^{hg^{\prime }})(C_{t}^{aa^{\prime
}}C_{t}^{bb^{\prime }}+C_{t}^{ab^{\prime }}C_{t}^{ba^{\prime }})dt, \\
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v;u^{\prime },v^{\prime })=(1,1;1,1) \\
\frac{3}{4{\Greekmath 0112} }\int_{0}^{T}(\partial _{gh}H\partial _{ab}G\partial
_{g^{\prime }h^{\prime }}H^{\prime }\partial _{a^{\prime }b^{\prime
}}G^{\prime })(C_{t})(C_{t}^{gg^{\prime }}C_{t}^{hh^{\prime
}}+C_{t}^{gh^{\prime }}C_{t}^{hg^{\prime }})\overline{C}_{t}^{ab,a^{\prime
}b^{\prime }}dt, \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v;u^{\prime },v^{\prime
})=(1,2;1,2) \\
\frac{3}{4{\Greekmath 0112} }\int_{0}^{T}(\partial _{gh}H\partial _{ab}G\partial
_{g^{\prime }h^{\prime }}H^{\prime }\partial _{a^{\prime }b^{\prime
}}G^{\prime })(C_{t})(C_{t}^{aa^{\prime }}C_{t}^{bb^{\prime
}}+C_{t}^{ab^{\prime }}C_{s}^{ba^{\prime }})\overline{C}_{t}^{gh,g^{\prime
}h^{\prime }}dt, \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v;u^{\prime },v^{\prime
})=(2,1;2,1) \\
\frac{151{\Greekmath 0112} }{280}\int_{0}^{T}(\partial _{gh}H\partial _{ab}G\partial
_{g^{\prime }h^{\prime }}H^{\prime }\partial _{a^{\prime }b^{\prime
}}G^{\prime })(C_{t})\overline{C}_{s}^{ab,a^{\prime }b^{\prime }}\overline{C}
_{t}^{gh,g^{\prime }h^{\prime }}dt, \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}
(u,v;u^{\prime },v^{\prime })=(2,2;2,2) \\
0 \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{otherwise}.
\end{cases}
\end{equation*}
Using equation ((ref)), we deduce that the asymptotic covariance
between $\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i,r}^{\left( A\right) }$
and $\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i,s}^{\left( A\right) }$
is given by
\begin{eqnarray*}
&&\sum_{g,h,a,b=1}^{d}\sum_{g^{\prime },h^{\prime },a^{\prime },b^{\prime
}=1}^{d}\sum_{u,v,u^{\prime },v^{\prime }=1}^{2}\Bigg(A\Big(
(H_{r},gh,u;G_{r},ab,v),(H_{s},g^{\prime }h^{\prime },u^{\prime
};G_{s},a^{\prime }b^{\prime },v^{\prime })\Big)_{T} \\
&&+A\Big((H_{r},gh,u;G_{r},ab,v),(H_{s},a^{\prime }b^{\prime },v^{\prime
};G_{s},g^{\prime }h^{\prime },u^{\prime })\Big)_{T} \\
&&+A\Big((H_{r},ab,v;G_{r},gh,u),(H_{s},g^{\prime }h^{\prime },u^{\prime
};G_{s},a^{\prime }b^{\prime },v^{\prime })\Big)_{T} \\
&&+A\Big((H_{r},ab,v;H_{r},gh,u),(H_{s},a^{\prime }b^{\prime },v^{\prime
};G_{s},g^{\prime }h^{\prime },u^{\prime })\Big)_{T}\Bigg).
\end{eqnarray*}
The above expression can be rewritten as
\begin{eqnarray*}
&&\sum_{g,h,a,b=1}^{d}\sum_{j,k,l,m=1}^{d}\Bigg(\frac{6}{{\Greekmath 0112} ^{3}}
\int_{0}^{T}\big(\partial _{gh}H_{r}\partial _{ab}G_{r}\partial
_{jk}H_{s}\partial _{lm}G_{s}(C_{t})\big)\Big[
(C_{t}^{gj}C_{t}^{hk}+C_{t}^{gk}C_{t}^{hj})(C_{t}^{al}C_{t}^{bm}+C_{t}^{am}C_{t}^{bl})
\\
&&+(C_{t}^{aj}C_{t}^{bk}+C_{t}^{ak}C_{t}^{bj})(C_{t}^{gl}C_{t}^{hm}+C_{t}^{gm}C_{t}^{hl})
\Big]dt \\
&&+\frac{151{\Greekmath 0112} }{140}\int_{0}^{t}\big(\partial _{gh}H_{r}\partial
_{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s}(C_{t})\big)\Big[\overline{C}
_{t}^{gh,jk}\overline{C}_{t}^{ab,lm}+\overline{C}_{t}^{ab,jk}\overline{C}
_{t}^{gh,lm}\Big]dt \\
&&+\frac{3}{2{\Greekmath 0112} }\int_{0}^{t}\big(\partial _{gh}H_{r}\partial
_{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s}(C_{t})\big)\Big[
(C_{t}^{gj}C_{t}^{hk}+C_{t}^{gk}C_{t}^{hj})\overline{C}
_{t}^{ab,lm}+(C_{t}^{al}C_{t}^{bm}+C_{t}^{am}C_{t}^{bl})\overline{C}
_{t}^{gh,jk} \\
&&+(C_{t}^{gl}C_{s}^{hm}+C_{t}^{gm}C_{s}^{hl})\overline{C}
_{t}^{ab,jk}+(C_{t}^{aj}C_{t}^{bk}+C_{t}^{ak}C_{t}^{bj})\overline{C}
_{t}^{gh,lm}\Big]dt\Bigg),
\end{eqnarray*}
which completes the proof.
\section{Proof of Theorem (ref)}
Recall that $N_{s}$ is the number of jumps in $C$ from time $0$ to $s$. Let
\begin{eqnarray*}
L^{\prime \prime }\left( n\right) &=&\left\{ i=k_{n}+1,k_{n}+2,...:N_{\left(
i+5\right) k_{n}\Delta _{n}}-N_{\left( i-1\right) k_{n}\Delta
_{n}}=0\right\} , \\
L^{\prime \prime }\left( n,T\right) &=&\left\{ i=1,2,...,\left[ T/\Delta _{n}
\right] -5k_{n}+1\right\} \cap L^{\prime \prime }\left( n\right) , \\
\overline{L}^{\prime \prime }\left( n,T\right) &=&\left\{ i=1,2,...,\left[
T/\Delta _{n}\right] -5k_{n}+1\right\} \backslash L^{\prime \prime }\left(
n\right) .
\end{eqnarray*}
Denote by $\widehat{{\Greekmath 0121} }_{T}^{r,s,(1)}$, $\widehat{{\Greekmath 0121} }
_{T}^{r,s,(2)} $, and $\widehat{{\Greekmath 0121} }_{T}^{r,s,(3)}$ the $i^{th}$ summand
of $\widehat{\Omega }_{T}^{r,s,(1)}$, $\widehat{\Omega }_{T}^{r,s,(2)}$, and
$\widehat{\Omega }_{T}^{r,s,(3)}$, without the volatility jump truncation,
so they satisfy
\begin{equation*}
\widehat{\Omega }_{T}^{r,s,(m)}=\sum_{i=k_{n}+1}^{[T/\Delta _{n}]-5k_{n}+1}
\widehat{{\Greekmath 0121} }_{T}^{r,s,(m)}1_{\left\{ A_{i}\cap A_{i+k_{n}}\cap
A_{i+2k_{n}}\cap A_{i+3k_{n}}\right\} }\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ for }m=1,2\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{, and }3\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{.
}
\end{equation*}
The same methods as in Theorems (ref) and (ref) can be used to show
\begin{eqnarray*}
\sum_{i\in L^{\prime \prime }\left( n,T\right) }\widehat{{\Greekmath 0121} }
_{T}^{r,s,(m)}1_{\left\{ A_{i}\cap A_{i+k_{n}}\cap A_{i+2k_{n}}\cap
A_{i+3k_{n}}\right\} }-\sum_{i\in L^{\prime \prime }\left( n,T\right) }
\widehat{{\Greekmath 0121} }_{T}^{r,s,(m)} &=&o_{p}\left( 1\right) \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ and} \\
\sum_{i\in \overline{L}^{\prime \prime }\left( n,T\right) }\widehat{{\Greekmath 0121} }
_{T}^{r,s,(m)}1_{\left\{ A_{i}\cap A_{i+k_{n}}\cap A_{i+2k_{n}}\cap
A_{i+3k_{n}}\right\} } &=&o_{p}\left( 1\right) .
\end{eqnarray*}
We conclude that the probability limit of $\widehat{\Omega }_{T}^{r,s,(m)}$
is the same as $\sum_{i\in L^{\prime \prime }\left( n,T\right) }\widehat{
{\Greekmath 0121} }_{T}^{r,s,(m)}$ for $m=1,2,3$.
Using boundedness of the derivatives of $H_{r},G_{r},H_{s}$ and $G_{s}$ and
Theorem 2.2 in jacodrosenbaum-sqrtn, one can show that
\begin{equation*}
\frac{6}{{\Greekmath 0112} ^{3}}\sum_{i\in L^{\prime \prime }\left( n,T\right) }
\widehat{{\Greekmath 0121} }_{T}^{r,s,(1)}\overset{\mathbb{P}}{\longrightarrow }\Sigma
_{T}^{r,s,(1)}.
\end{equation*}
Next, by equation (3.27) in jacodrosenbaum-sqrtn, we have
\begin{equation*}
\frac{3}{2{\Greekmath 0112} }\left( \sum_{i\in L^{\prime \prime }\left( n,T\right) }
\widehat{{\Greekmath 0121} }_{T}^{r,s,(3)}-\frac{6}{{\Greekmath 0112} }\sum_{i\in L^{\prime \prime
}\left( n,T\right) }\widehat{{\Greekmath 0121} }_{T}^{r,s,(1)}\right) \overset{\mathbb{P
}}{\longrightarrow }\Sigma _{T}^{r,s,(3)}.
\end{equation*}
Finally, to show that
\begin{equation*}
\frac{151{\Greekmath 0112} }{140}\frac{9}{4{\Greekmath 0112} ^{2}}\left( \sum_{i\in L^{\prime
\prime }\left( n,T\right) }\widehat{{\Greekmath 0121} }_{T}^{r,s,(2)}+\frac{4}{{\Greekmath 0112}
^{2}}\sum_{i\in L^{\prime \prime }\left( n,T\right) }\widehat{{\Greekmath 0121} }
_{T}^{r,s,(1)}-\frac{4}{3}\sum_{i\in L^{\prime \prime }\left( n,T\right) }
\widehat{{\Greekmath 0121} }_{T}^{r,s,(3)}\right) \overset{\mathbb{P}}{\longrightarrow }
\Sigma _{T}^{r,s,(2)},
\end{equation*}
we first observe that the approximation error induced by replacing $\widehat{
C}_{i}^{n}$ by $\widehat{C}_{i}^{^{\prime }n}$ in Theorem (ref) is
negligible.\newline
For $1\leq g,h,a,b,j,k,l,m\leq d$ and $1\leq r,s\leq d$, we define
\begin{align*}
\widehat{W}_{T}^{n}& =\sum_{i\in L^{\prime \prime }\left( n,T\right)
}(\partial _{gh}H_{r}\partial _{ab}G_{r}\partial _{gh}H_{s}\partial
_{lm}G_{s})(\widehat{C}_{i}^{n}){\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115}
_{i}^{n,jk}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}{\Greekmath 0115} _{i+2k_{n}}^{n,lm}, \\
\widehat{w}(1)_{i}^{n}& =(\partial _{gh}H_{r}\partial _{ab}G_{r}\partial
_{jk}H_{s}\partial _{lm}G_{s})(C_{i-1}^{n})\mathbb{E}({\Greekmath 0115}
_{i}^{n,gh}{\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}{\Greekmath 0115}
_{i+2k_{n}}^{n,lm}|\mathcal{F}_{i}^{n}), \\
\widehat{w}(2)_{i}^{n}& =(\partial _{gh}H_{r}\partial _{ab}G_{r}\partial
_{jk}H_{s}\partial _{lm}G_{s})(C_{i-1}^{n})({\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115}
_{i}^{n,jk}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}{\Greekmath 0115} _{i+2k_{n}}^{n,lm}-\mathbb{E}
({\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}{\Greekmath 0115}
_{i+2k_{n}}^{n,lm}|\mathcal{F}_{i}^{n})), \\
\widehat{w}(3)_{i}^{n}& =\Big((\partial _{gh}H_{r}\partial
_{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s})(\widehat{C}
_{i}^{n})-(\partial _{gh}H_{r}\partial _{ab}G_{r}\partial _{jk}H_{s}\partial
_{lm}G_{s})(C_{i-1}^{n})\Big){\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115}
_{i+2k_{n}}^{n,ab}{\Greekmath 0115} _{i+2k_{n}}^{n,lm}, \\
\widehat{W}(u)_{t}^{n}& =\sum_{i\in L^{\prime \prime }\left( n,T\right) }
\widehat{w}_{i}(u),u=1,2,3.
\end{align*}
Now, note that we also have $\widehat{W}_{t}^{n}=\widehat{W}(1)_{t}^{n}+
\widehat{W}(2)_{t}^{n}+\widehat{W}(3)_{t}^{n}$. By Taylor expansion and
using repeatedly the boundedness of $C_{t}$, we obtain, for $i\in L^{\prime
\prime }\left( n,T\right) $
\begin{equation*}
|\widehat{w}(3)_{i}^{n}|\leq K\Vert {\Greekmath 0117} _{i}^{n}\Vert \Vert {\Greekmath 0115}
_{i}^{n}\Vert ^{2}\Vert {\Greekmath 0115} _{i+2k_{n}}^{n}\Vert ^{2},
\end{equation*}
which implies $\mathbb{E}(|\widehat{w}(3)_{i}^{n}|)\leq K\Delta _{n}^{5/4}$
and hence $\widehat{W}(3)_{t}^{n}\overset{\mathbb{P}}{\longrightarrow }0$.
Using Cauchy-Schwartz inequality and the bound $\mathbb{E}(\Vert {\Greekmath 0115}
_{i}^{n}\Vert ^{q}|\mathcal{F}_{i}^{n})\leq K\Delta _{n}^{q/4}$, we have $
\mathbb{E}(|\widehat{w}(2)_{i}^{n}|^{2})\leq K\Delta _{n}^{2}$ for $i\in
L^{\prime \prime }\left( n,T\right) $. Observing furthermore that $\widehat{w
}(2)_{i}^{n}$ is $\mathcal{F}_{i+4k_{n}}-$measurable, Lemma B.8 in yacjacod14 implies $\widehat{W}(2)_{t}^{n}\overset{\mathbb{P}}{
\longrightarrow }0$. \newline
Next, define
\begin{align*}
& w_{i}^{n}=(\partial _{gh}H_{r}\partial _{ab}G_{r}\partial
_{jk}H_{s}\partial _{lm}G_{s})(C_{i-1}^{n})\Big[\frac{4}{k_{n}^{2}\Delta _{n}}
(C_{i-1}^{n,ga}C_{i-1}^{n,hb}+C_{i-1}^{n,gb}C_{i-1}^{n,ha})(C_{i-1}^{n,jl}C_{i-1}^{n,km}+C_{i-1}^{n,jm}C_{i-1}^{n,kl})
\\
& +\frac{4}{3}(C_{i-1}^{n,jl}C_{i-1}^{n,km}+C_{i-1}^{n,jm}C_{i-1}^{n,kl})
\overline{C}_{i-1}^{n,gh,ab}+\frac{4}{3}
(C_{i-1}^{n,ga}C_{i-1}^{n,hb}+C_{i-1}^{n,gb}C_{i-1}^{n,ha})\overline{C}_{i-1}^{n,jk,lm}
\\
& +\frac{4(k_{n}^{2}\Delta _{n})}{9}\overline{C}_{i-1}^{n,gh,ab}
\overline{C}_{i-1}^{n,jk,lm}\Big], \\
& W_{T}^{n}=\Delta _{n}\sum_{i\in L^{\prime \prime }\left( n,T\right)
}w_{i}^{n}.
\end{align*}
Using the cadlag property of $c$ and $\overline{C}$, $k_{n}\sqrt{\Delta _{n}}
\rightarrow {\Greekmath 0112} $, and the Riemann integral convergence, we conclude that
$W_{T}^{n}\overset{\mathbb{P}}{\longrightarrow }W_{T}$ where
\begin{align*}
& W_{T}=\int_{0}^{T}(\partial _{gh}H_{r}\partial _{ab}G_{r}\partial
_{jk}H_{s}\partial _{lm}G_{s})(C_{t})\Big[\frac{4}{{\Greekmath 0112} ^{2}}
(C_{t}^{ga}C_{t}^{hb}+C_{t}^{gb}C_{t}^{ha})(C_{t}^{jl}C_{t}^{km}+C_{t}^{jm}C_{t}^{kl})
\\
& +\frac{4}{3}(C_{t}^{jl}C_{t}^{km}+C_{t}^{jm}C_{t}^{kl})\overline{C}
_{t}^{gh,ab}+\frac{4}{3}(C_{t}^{ga}C_{i}^{hb}+C_{t}^{gb}C_{t}^{ha})\overline{
C}_{t}^{jk,lm}+\frac{4{\Greekmath 0112} ^{2}}{9}\overline{C}_{t}^{gh,ab}\overline{C}
_{t}^{jk,lm}\Big]dt.
\end{align*}
In addition, by Lemma (ref), it holds that
\begin{equation*}
\mathbb{E}(|\widehat{W}(1)_{T}^{n}-W_{T}^{n}|)\leq \Delta _{n}\mathbb{E}
\Bigg(\sum_{i\in L^{\prime \prime }\left( n,T\right) }(\Delta
_{n}^{1/8}+{\Greekmath 0111} _{i,4k_{n}})\Bigg).
\end{equation*}
Hence, by the third result of Lemma (ref) we have $\widehat{W}
_{T}^{n}\overset{\mathbb{P}}{\longrightarrow }W_{t}$, from which it follows
that
\begin{align*}
& \frac{9}{4{\Greekmath 0112} ^{2}}\Big[\widehat{W}(1)_{T}^{n}+\frac{4}{k_{n}^{2}}
\sum_{i\in L^{\prime \prime }\left( n,T\right) }(\partial _{gh}H_{r}\partial
_{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s})(\widehat{C}
_{i}^{n})[C_{i}^{n}(jk,lm)C_{i}^{n}(gh,ab)] \\
& -\frac{2}{k_{n}}\sum_{i\in L^{\prime \prime }\left( n,T\right) }(\partial
_{gh}H_{r}\partial _{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s})(
\widehat{C}_{i}^{n})C_{i}^{n}(gh,ab){\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm} \\
& -\frac{2}{k_{n}}\sum_{i\in L^{\prime \prime }\left( n,T\right) }(\partial
_{gh}H_{r}\partial _{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s})(
\widehat{C}_{i}^{n})C_{i}^{n}(jk,lm){\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115} _{i}^{n,ab}
\Big] \\
& \overset{\mathbb{P}}{\longrightarrow }\int_{0}^{T}(\partial
_{gh}H_{r}\partial _{ab}G_{r}\partial _{jk}H_{s}\partial _{lm}G_{s})(C_{t})
\overline{C}_{t}^{gh,ab}\overline{C}_{t}^{jk,lm}dt.
\end{align*}
The result follows from the above convergence, the already invoked symmetry
argument, and straightforward calculations.
\section{Proofs of Auxiliary Lemmas and Theorems}
This section is devoted to the proofs of the
auxiliary theorems and lemmas (listed in Section (ref)) that
were used to prove Theorem (ref) and Theorem (ref).
\subsection{Proof of Theorem (ref)}
The proof proceeds in three steps. In Step 1, we prove, for $i\in L\left(
n,T\right) $,
\begin{equation}
P\left( \overline{A}_{i}\right) \leq Ka_{n}\Delta _{n}^{\left( 2-r\right)
{\Greekmath 0124} -{\Greekmath 0124} ^{\prime }},
\end{equation}
where $a_{n}$ is a sequence converging to zero, and $\overline{A}_{i}$ is
the complement of $A_{i}$. In Step 2, we prove, for $p\geq 1$ and $i\in
L\left( n,T\right) $,
\begin{equation}
E\left[ \left\vert {\Greekmath 0123} _{i}\right\vert ^{p}\right] \leq K\Delta
_{n}^{p}+Ka_{n}\Delta _{n}^{\left( 4p-r\right) {\Greekmath 0124} +1-\frac{3}{2}p}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{.
}
\end{equation}
Step 3 completes the proof of Theorem (ref).
Step 1. We now prove equation ((ref)). Recall $\widehat{C}_{i}^{\prime n}$ notation in ((ref)). For $i\in L\left( n,T\right) $,
\begin{eqnarray}
P\left( \overline{A}_{i}\right) &=&P\left( \left\Vert \widehat{C}
_{i+k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{n}\right\Vert \geq u_{n}^{\prime
}\right) \notag \\
&\leq &P\left( \left\Vert \widehat{C}_{i+k_{n}}^{\prime n}-\widehat{C}
_{i-k_{n}}^{\prime n}\right\Vert +\left\Vert \widehat{C}_{i+k_{n}}^{n}-
\widehat{C}_{i+k_{n}}^{\prime n}\right\Vert +\left\Vert \widehat{C}
_{i-k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{\prime n}\right\Vert \geq
u_{n}^{\prime }\right) \notag \\
&\leq &P\left( \left\Vert \widehat{C}_{i+k_{n}}^{\prime n}-\widehat{C}
_{i-k_{n}}^{\prime n}\right\Vert \geq \frac{u_{n}^{\prime }}{2}\right)
+P\left( \left\Vert \widehat{C}_{i+k_{n}}^{n}-\widehat{C}_{i+k_{n}}^{\prime
n}\right\Vert +\left\Vert \widehat{C}_{i-k_{n}}^{n}-\widehat{C}
_{i-k_{n}}^{\prime n}\right\Vert \geq \frac{u_{n}^{\prime }}{2}\right) .
\end{eqnarray}
Using standard results in the literature, we have for $q\geq 2$ and $i\in
L\left( n,T\right) $,
\begin{equation}
E\left( \left\Vert \widehat{C}_{i+k_{n}}^{\prime n}-\widehat{C}
_{i-k_{n}}^{\prime n}\right\Vert ^{q}\right) \leq K\Delta _{n}^{q/4},
\end{equation}
see, for example, equation (3.26) in jacodrosenbaum-sqrtn. Therefore,
the first term in ((ref)) satisfies, by Markov's inequality,
for $p\geq 2$,
\begin{equation}
P\left( \left\Vert \widehat{C}_{i+k_{n}}^{\prime n}-\widehat{C}
_{i-k_{n}}^{\prime n}\right\Vert \geq \frac{u_{n}^{\prime }}{2}\right) \leq
K\Delta _{n}^{p/4-{\Greekmath 0124} ^{\prime }p}.
\end{equation}
By (4.8) in jacodrosenbaum13, there exists a sequence of real numbers $
a_{n} $ converging to zero such that
\begin{equation}
\mathbb{E}(\Vert \widehat{C}_{i}^{n}-\widehat{C}_{i}^{^{\prime }n}\Vert
^{q})\leq K_{q}a_{n}\Delta _{n}^{(2q-r){\Greekmath 0124} +1-q}, \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for any}q\geq 1,
\end{equation}
where for later use, we note that this result also holds in the presence of
volatility jumps. Therefore, the second term in ((ref)) satisfies, by Markov's inequality,
\begin{eqnarray}
&&P\left( \left\Vert \widehat{C}_{i+k_{n}}^{n}-\widehat{C}_{i+k_{n}}^{\prime
n}\right\Vert +\left\Vert \widehat{C}_{i-k_{n}}^{n}-\widehat{C}
_{i-k_{n}}^{\prime n}\right\Vert \geq \frac{u_{n}^{\prime }}{2}\right)
\notag \\
&\leq &\frac{1}{u_{n}^{\prime }/2}E\left( \left\Vert \widehat{C}
_{i+k_{n}}^{n}-\widehat{C}_{i+k_{n}}^{\prime n}\right\Vert +\left\Vert
\widehat{C}_{i-k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{\prime n}\right\Vert
\right) \leq Ka_{n}\Delta _{n}^{\left( 2-r\right) {\Greekmath 0124} -{\Greekmath 0124} ^{\prime }}.
\end{eqnarray}
Since ${\Greekmath 0124} ^{\prime }<\frac{1}{8}$ and by choosing sufficiently large
$p$ in ((ref)), equations ((ref)) and ((ref)) give ((ref)).
Step 2. We now prove equation ((ref)). First,
note that for $q\geq 1$, by ((ref)),
\begin{equation}
\mathrm{E}\left( \left\Vert \widehat{C}_{i\Delta _{n}}^{n}\right\Vert
^{q}\right) \leq K\mathrm{E}\left[ \left\vert \widehat{C}_{i\Delta _{n}}^{n}-
\widehat{C}_{i\Delta _{n}}^{\prime n}\right\vert ^{q}\right] +K\mathrm{E}
\left[ \left\vert \widehat{C}_{i\Delta _{n}}^{\prime n}\right\vert ^{q}
\right] \leq Ka_{n}\Delta _{n}^{\left( 2q-r\right) {\Greekmath 0124} +1-q}+K.
\end{equation}
By Taylor expansion and $H$ and $G$ having bounded derivatives, for $
i\in L\left( n,T\right) $ and $p\geq 1$,
\begin{eqnarray}
&&E\left[ \left\vert {\Greekmath 0123} _{i}\right\vert ^{p}\right] \notag \\
&\leq & K\frac{1}{
k_{n}^{p}}\mathrm{E}\left( \left\Vert \widehat{C}_{\left( i+k_{n}\right)
\Delta _{n}}^{n}-\widehat{C}_{i\Delta _{n}}^{n}\right\Vert ^{2p}\right) +K
\frac{1}{k_{n}^{2p}}\mathrm{E}\left( \left\Vert \widehat{C}_{i\Delta
_{n}}^{n}\right\Vert ^{2p}\right) \notag \\
&\leq & K\Delta
_{n}^{p/2}\mathrm{E}\left( \left\Vert \widehat{C}_{\left( i+k_{n}\right)
\Delta _{n}}^{\prime n}-\widehat{C}_{i\Delta _{n}}^{\prime n}\right\Vert
^{2p}+\left\Vert \widehat{C}_{\left( i+k_{n}\right) \Delta _{n}}^{n}-
\widehat{C}_{\left( i+k_{n}\right) \Delta _{n}}^{\prime n}\right\Vert
^{2p}+\left\Vert \widehat{C}_{i\Delta _{n}}^{n}-\widehat{C}_{i\Delta
_{n}}^{n\prime }\right\Vert ^{2p}\right) +K\Delta _{n}^{p}\mathrm{E}\left(
\left\Vert \widehat{C}_{i\Delta _{n}}^{n}\right\Vert ^{2p}\right) \notag \\
&\leq &K\Delta _{n}^{p/2}\left( \Delta _{n}^{p/2}+a_{n}\Delta _{n}^{\left(
4p-r\right) {\Greekmath 0124} +1-2p}\right) +K\Delta _{n}^{p}\left[ Ka_{n}\Delta
_{n}^{\left( 4p-r\right) {\Greekmath 0124} +1-2p}+K\right] \notag \\
&=&K\Delta _{n}^{p}+Ka_{n}\Delta _{n}^{\left( 4p-r\right) {\Greekmath 0124} +1-\frac{3}{
2}p},
\end{eqnarray}
where the third inequality uses ((ref)), ((ref)) and ((ref)).
Step 3. We now complete the proof of Theorem (ref). By the triangle
and Cauchy-Schwarz inequalities,
\begin{eqnarray*}
&&E\left\vert \sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}1_{\left\{
A_{i}\cap A_{i+k_{n}}\right\} }-\sum_{i\in L\left( n,T\right) }{\Greekmath 0123}
_{i}\right\vert \\
&\leq &\sum_{i\in L\left( n,T\right) }E\left\vert {\Greekmath 0123} _{i}\left(
1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\} }-1\right) \right\vert \\
&\leq &\sum_{i\in L\left( n,T\right) }\sqrt{E\left\vert {\Greekmath 0123}
_{i}^{2}\right\vert }\sqrt{P\left( \overline{A_{i}}\cup \overline{A_{i+k_{n}}
}\right) } \\
&\leq &\sum_{i\in L\left( n,T\right) }\sqrt{E\left\vert {\Greekmath 0123}
_{i}^{2}\right\vert }\sqrt{P\left( \overline{A_{i}}\right) +P\left(
\overline{A_{i+k_{n}}}\right) } \\
&\leq &K\Delta _{n}^{-1}\left( \Delta _{n}^{\left( 8-r\right) {\Greekmath 0124}
-2}\right) ^{1/2}\left( a_{n}\Delta _{n}^{\left( 2-r\right) {\Greekmath 0124} -{\Greekmath 0124}
^{\prime }}\right) ^{1/2} \\
&=&\Delta _{n}^{l\left( {\Greekmath 0124} ,{\Greekmath 0124} ^{\prime }\right) },
\end{eqnarray*}
where 4th inequality follows by ((ref)) with $p=2$, and
((ref)). In the
above,
\begin{equation*}
l\left( {\Greekmath 0124} ,{\Greekmath 0124} ^{\prime }\right) =-1+\frac{1}{2}\left[ \left(
8-r\right) {\Greekmath 0124} -2\right] +\frac{1}{2}\left[ \left( 2-r\right) {\Greekmath 0124}
-{\Greekmath 0124} ^{\prime }\right] .
\end{equation*}
A straightforward calculation shows that ${\Greekmath 0124} >
\frac{2{\Greekmath 0124} ^{\prime }+9}{4\left( 5-r\right) }$ implies $l\left( {\Greekmath 0124}
,{\Greekmath 0124} ^{\prime }\right) > \frac{1}{4}$, which completes the proof of
Theorem (ref).
\subsection{Proof of Theorem (ref)}
Without loss of generality, we can assume that there is at most one
volatility jump in $\left( \left( i-k_{n}\right) \Delta _{n},\left(
i+3k_{n}\right) \Delta _{n}\right] $ for any $i\in \overline{L}\left(
n,T\right) $. To study the behavior of ${\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap
A_{i+k_{n}}\right\} }$ on $i\in \overline{L}\left( n,T\right) $, we will
distinguish between two cases, depending on whether or not there is a
volatility jump in $\left( i\Delta _{n},\left( i+2k_{n}\right) \Delta _{n}
\right] $. So define $B_{i}$ as the event that there is a volatility jump in
$\left( i\Delta _{n},\left( i+2k_{n}\right) \Delta _{n}\right] $ (we omit
indexing $B_{i}$\ by $n$ for brevity). Denote by $\overline{B}_{i}$ the
complement of $B_{i}$. Intuitively, for $i\in \overline{L}\left( n,T\right) $
, ${\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\} }$ is small
because, on the one hand, $P\left( A_{i}\cap A_{i+k_{n}}\right) $ is small
on $B_{i}$, on the other hand, ${\Greekmath 0123} _{i}$ is small on $\overline{B}
_{i} $.
We have
\begin{eqnarray}
&&\mathrm{E}\left\vert \sum_{i\in \overline{L}\left( n,T\right) }{\Greekmath 0123}
_{i}1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\} }\right\vert =\mathrm{E}
\left\vert \sum_{i\in \overline{L}\left( n,T\right) :B_{i}}{\Greekmath 0123}
_{i}1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\} }+\sum_{i\in \overline{L}\left(
n,T\right) :\overline{B}_{i}}{\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap
A_{i+k_{n}}\right\} }\right\vert \notag \\
&\leq &\sum_{i\in \overline{L}\left( n,T\right) :B_{i}}\mathrm{E}\left\vert
{\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\} }\right\vert
+\sum_{i\in \overline{L}\left( n,T\right) :\overline{B}_{i}}\mathrm{E}
\left\vert {\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\}
}\right\vert ,
\end{eqnarray}
where \textquotedblleft $i\in \overline{L}\left( n,T\right) :B_{i}$
\textquotedblright\ denotes those terms in $\overline{L}\left( n,T\right) $,
for which $B_{i}$ is true.
First, we show that the second term in ((ref)) is $
o_{p}\left( \Delta _{n}^{1/4}\right) $. For $i\in \overline{L}\left(
n,T\right) $ such that $B_{i}$ if false, we can use the bound on $\mathrm{E}
\left[ \left\vert {\Greekmath 0123} _{i}\right\vert ^{p}\right] $ in ((ref)) for $p\geq 1$. The second term in (
(ref)) satisfies
\begin{eqnarray*}
&&\sum_{i\in \overline{L}\left( n,T\right) :\overline{B}_{i}}\mathrm{E}
\left\vert {\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\}
}\right\vert \\
&\leq &\sum_{i\in \overline{L}\left( n,T\right) :\overline{B}_{i}}\mathrm{E}
\left\vert {\Greekmath 0123} _{i}\right\vert \\
&\leq &K\Delta _{n}^{-1/2}\left( \Delta _{n}+\Delta _{n}^{\left( 4-r\right)
{\Greekmath 0124} -\frac{1}{2}}\right) \\
&=&K\Delta _{n}^{1/2}+K\Delta _{n}^{\left( 4-r\right) {\Greekmath 0124} -1}.
\end{eqnarray*}
Theorem 1 assumptions imply $\left( 4-r\right) {\Greekmath 0124} -1> \frac{1}{4}$,
so the second term in ((ref)) is $o_{p}\left( \Delta
_{n}^{1/4}\right) $.
The rest of the proof is devoted to showing that the first term in ((ref)) is $o_{p}\left( \Delta _{n}^{1/4}\right) $. This will
complete the proof of Theorem (ref).
The first term in ((ref)) involves those $i\in \overline{L}
\left( n,T\right) $, for which $B_{i}$ is true. We will show below that
\begin{equation}
P\left( A_{i}\cap A_{i+k_{n}}\right) \leq K\Delta _{n}^{1/2}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ for }i\in
\overline{L}\left( n,T\right) \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ such that }B_{i}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ holds.}
\end{equation}
We use the following bound in the presence of the volatility jump,
\begin{equation}
\mathrm{E}\left( \left\vert {\Greekmath 0123} _{i}\right\vert ^{p}\right) \leq K
\frac{1}{k_{n}^{p}}\left[ \mathrm{E}\left[ \left\vert \widehat{C}
_{i}^{n}\right\vert ^{p}\right] +\mathrm{E}\left[ \left\vert \widehat{C}
_{i+k_{n}}^{n}\right\vert ^{p}\right] \right] \leq K\frac{1}{k_{n}^{p}}
\left( a_{n}\Delta _{n}^{\left( 2p-r\right) {\Greekmath 0124} +1-p}+K\right) ,
\end{equation}
where the first inequality uses Taylor expansion and bounded derivatives of $
H$ and $G$, and the last transition uses ((ref)).
The first term in ((ref)) satisfies, for $p\geq 1$, by
Holder inequality, ((ref)) and ((ref)),
\begin{eqnarray*}
&&\sum_{i\in \overline{L}\left( n,T\right) :B_{i}}\mathrm{E}\left\vert
{\Greekmath 0123} _{i}1_{\left\{ A_{i}\cap A_{i+k_{n}}\right\} }\right\vert \\
&\leq &\sum_{i\in \overline{L}\left( n,T\right) :B_{i}}\left( \mathrm{E}
\left[ \left\vert {\Greekmath 0123} _{i}\right\vert ^{p}\right] \right) ^{1/p}\left(
\mathrm{P}\left( A_{i}\cap A_{i+k_{n}}\right) \right) ^{\left( p-1\right) /p}
\\
&\leq &\sum_{i\in \overline{L}\left( n,T\right) :B_{i}}K\left[ \Delta
_{n}\times \left( \Delta _{n}^{\left( 2p-r\right) {\Greekmath 0124} +1-p}+1\right)
\right] ^{1/p}\left[ \Delta _{n}^{1/2}\right] ^{\left( p-1\right) /p} \\
&=&\sum_{i\in \overline{L}\left( n,T\right) :B_{i}}K\Delta _{n}^{l\left(
r,{\Greekmath 0124} \right) }.
\end{eqnarray*}
Since the number of terms in $\overline{L}\left( n,T\right) $ is bounded by $
Kk_{n}$ ($k_{n}$ arises due to overlapping blocks defining ${\Greekmath 0123} _{i}$
), the first term in ((ref)) is $o_{p}\left( \Delta
_{n}^{1/4}\right) $ if $l\left( r,{\Greekmath 0124} \right) >\frac{3}{4}$. To study $
l\left( r,{\Greekmath 0124} \right) $, we distinguish two cases, depending on whether $
\left( 2p-r\right) {\Greekmath 0124} +1-p\geq 0$ holds.
Case 1. When $\left( 2p-r\right) {\Greekmath 0124} +1-p\geq 0$, $l\left( r,{\Greekmath 0124}
\right) =\frac{1}{2p}\left( p+1\right) $, so $l\left( r,{\Greekmath 0124} \right) >
\frac{3}{4}$ if $p<2.$
Case 2. When $\left( 2p-r\right) {\Greekmath 0124} +1-p<0$, $l\left( r,{\Greekmath 0124}
\right) =\frac{1}{p}\left( \left( 2p-r\right) {\Greekmath 0124} +1-p\right) +\frac{p-1}{
2p}$. We have $l\left( r,{\Greekmath 0124} \right) >\frac{3}{4}$ if ${\Greekmath 0124} >\frac{5p-6
}{4\left( 2p-r\right) }$. This is satisfied if we choose, for example, $
p=1.5 $.
The last step in the proof of Theorem (ref) is to show
that ((ref)) is true. In order to do that, we first
prove that if there is a volatility jump on $\left( i\Delta _{n},\left(
i+k_{n}\right) \Delta _{n}\right] $, then
\begin{equation}
P\left( \left\Vert \widehat{C}_{i+k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{n}\right
\Vert <u_{n}^{\prime }\right) =o_{p}\left( \Delta _{n}^{1/4}\right) .
\end{equation}
Denote by $S$ the time of the volatility jump on $\left( i\Delta _{n},\left(
i+k_{n}\right) \Delta _{n}\right] $, so the jump is $\Delta C_{S}$. Denote $
{\Greekmath 0118} _{n}\equiv \widehat{C}_{i+k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{n}-\Delta
C_{S}$, so $\widehat{C}_{i+k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{n}=\Delta
C_{S}+{\Greekmath 0118} _{n}$. We know ${\Greekmath 0118} _{n}=o_{p}\left( 1\right) $.
We know that there exists ${\Greekmath 010F} $, independent of $i$ or $S$,
such that $\left\Vert \Delta C\right\Vert > {\Greekmath 010F} $.
We will first show that if there is a volatility jump on $\left( i\Delta
_{n},\left( i+k_{n}\right) \Delta _{n}\right] $, for $s\geq 0$, it follows
that
\begin{equation}
P\left( \left\Vert \widehat{C}_{i+k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{n}\right
\Vert <u_{n}^{\prime }\right) \leq \frac{E\left( \left\Vert \widehat{C}
_{i+k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{n}-\Delta C_{S}\right\Vert ^{s}\right)
}{\left( {\Greekmath 010F} /2\right) ^{s}}.
\end{equation}
To prove ((ref)), note that the reverse
triangle inequality gives $\left\Vert \widehat{C}_{i+k_{n}}^{n}-\widehat{C}
_{i-k_{n}}^{n}\right\Vert =\left\Vert \Delta C+{\Greekmath 0118} _{n}\right\Vert \geq
\left\vert \left\Vert \Delta C\right\Vert -\left\Vert {\Greekmath 0118} _{n}\right\Vert
\right\vert $. Thus,
\begin{eqnarray*}
&&P\left( \left\Vert \widehat{C}_{i+k_{n}}^{n}-\widehat{C}
_{i-k_{n}}^{n}\right\Vert <u_{n}^{\prime }\right) \\
&\leq &P\left( \left\vert \left\Vert \Delta C\right\Vert -\left\Vert {\Greekmath 0118}
_{n}\right\Vert \right\vert <u_{n}^{\prime }\right) \\
&\leq &P\left( \left\Vert {\Greekmath 0118} _{n}\right\Vert >\frac{{\Greekmath 010F} }{2}\right) ,
\end{eqnarray*}
where the second inequality follows by distinguishing two cases, depending
on whether $\left\Vert \Delta C\right\Vert \geq \left\Vert {\Greekmath 0118}
_{n}\right\Vert $. Case 1:\ if $\left\Vert \Delta C\right\Vert \geq
\left\Vert {\Greekmath 0118} _{n}\right\Vert $, $\left\{ \left\vert \left\Vert \Delta
C\right\Vert -\left\Vert {\Greekmath 0118} _{n}\right\Vert \right\vert <u_{n}^{\prime
}\right\} =\left\{ \left\Vert \Delta C\right\Vert -\left\Vert {\Greekmath 0118}
_{n}\right\Vert <u_{n}^{\prime }\right\} =\left\{ \left\Vert \Delta
C\right\Vert -u_{n}^{\prime }<\left\Vert {\Greekmath 0118} _{n}\right\Vert \right\} $, so
we deduce $\left\{ {\Greekmath 010F} -u_{n}^{\prime }<\left\Vert {\Greekmath 0118} _{n}\right\Vert
\right\} $. For $n\,\ $large enough, this implies $\left\{ \left\Vert {\Greekmath 0118}
_{n}\right\Vert >\frac{{\Greekmath 010F} }{2}\right\} $ since $u_{n}^{\prime
}\rightarrow 0$. Case 2:\ if $\left\Vert \Delta C\right\Vert <\left\Vert {\Greekmath 0118}
_{n}\right\Vert $, we have $P\left( \left\{ \left\vert \left\Vert \Delta
C\right\Vert -\left\Vert {\Greekmath 0118} _{n}\right\Vert \right\vert <u_{n}^{\prime
}\right\} \cap \left\{ \left\Vert \Delta C\right\Vert <\left\Vert {\Greekmath 0118}
_{n}\right\Vert \right\} \right) \leq P\left( \left\Vert {\Greekmath 0118} _{n}\right\Vert
>\left\Vert \Delta C\right\Vert \right) \leq P\left( \left\Vert {\Greekmath 0118}
_{n}\right\Vert >{\Greekmath 010F} \right) \leq P\left( \left\Vert {\Greekmath 0118}
_{n}\right\Vert >\frac{{\Greekmath 010F} }{2}\right) $. Finally, ((ref)) follows by Markov's inequality.
By ((ref)), we obtain, for $s\geq 2$,
\begin{eqnarray}
P\left( \left\Vert \widehat{C}_{i+k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{n}\right
\Vert <u_{n}^{\prime }\right) &\leq &\frac{E\left( \left\Vert \widehat{C}
_{i+k_{n}}^{n}-\widehat{C}_{i-k_{n}}^{n}-\Delta C\right\Vert ^{s}\right) }{
\left( {\Greekmath 010F} /2\right) ^{s}} \notag \\
&\leq &KE\left( \left\Vert \widehat{C}_{i-k_{n}}^{n}-C_{S-}\right\Vert
^{s}\right) +KE\left( \left\Vert \widehat{C}_{i+k_{n}}^{n}-C_{S}\right\Vert
^{s}\right) .
\end{eqnarray}
The first term in ((ref)) satisfies, for $s\geq 2,$
by ((ref)) and ((ref))
\begin{eqnarray*}
E\left( \left\Vert \widehat{C}_{i-k_{n}}^{n}-C_{S-}\right\Vert ^{s}\right)
&\leq &KE\left( \left\Vert \widehat{C}_{i-k_{n}}^{n}-\widehat{C}
_{i-k_{n}}^{n\prime }\right\Vert ^{s}\right) +KE\left( \left\Vert \widehat{C}
_{i-k_{n}}^{n\prime }-C_{S-}\right\Vert ^{s}\right) \\
&\leq &K_{q}a_{n}\Delta _{n}^{\left( 2s-r\right) {\Greekmath 0124} +1-s}+K\Delta
_{n}^{s/4}.
\end{eqnarray*}
The second term in ((ref)) has the same bound by
the same arguments as the first term. Choosing $s=2$ in the above, and
taking into account that $\left( 2-r\right) {\Greekmath 0124} \geq \frac{3}{4}$ and $
{\Greekmath 0124} \geq \frac{3}{8}$, we obtain ((ref)).
Given ((ref)), it is simple to obtain ((ref)) as follows. By ((ref)),
if there is a jump on $\left( i\Delta _{n},\left( i+k_{n}\right) \Delta _{n}
\right] $, we know $P\left( A_{i}\right) =o_{p}\left( \Delta
_{n}^{1/4}\right) $, thus $\left( A_{i}\cap A_{i+k_{n}}\right) \leq P\left(
A_{i}\right) =o_{p}\left( \Delta _{n}^{1/4}\right) $. Applying ((ref)) with $i+k_{n}$ instead of $i$, if there is a
jump on $\left( \left( i+k_{n}\right) \Delta _{n},\left( i+2k_{n}\right)
\Delta _{n}\right] $, $P\left(
A_{i+k_{n}}\right) =o_{p}\left( \Delta _{n}^{1/4}\right) $. Thus, $P\left(
A_{i}\cap A_{i+k_{n}}\right) \leq P\left( A_{i+k_{n}}\right) =o_{p}\left(
\Delta _{n}^{1/4}\right) $. We conclude that if there is a jump on $\left(
i\Delta _{n},\left( i+2k_{n}\right) \Delta _{n}\right] $, i.e., event $B_{i}$
is true, then $\left( A_{i}\cap A_{i+k_{n}}\right) \leq P\left(
A_{i+k_{n}}\right) =o_{p}\left( \Delta _{n}^{1/4}\right) $. This concludes
the proof of ((ref)) and hence Theorem (ref).
\subsection{Proof of Theorem (ref)}
To show this result, let us define the functions
\begin{align*}
R(x,y)& =\sum_{g,h,a,b=1}^{d}\Big(\partial _{gh}H\partial _{ab}G\big)(x)\big(
y^{gh}-x^{gh}\Big)\Big(y^{ab}-x^{ab}\Big) \\
S(x,y)& =\Big(H(y)-H(x)\Big)\Big(G(y)-G(x)\Big) \\
U(x)& =\sum_{g,h,a,b=1}^{d}\Big(\partial _{gh}H\partial _{ab}G\Big)(x)\Big(
x^{ga}x^{hb}+x^{gb}x^{ha}\Big),
\end{align*}
for any $\mathbb{R}^{d}\times \mathbb{R}^{d}$ matrices $x$ and $y$. The
following decompositions hold,
\begin{align*}
& \sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{AN}-\sum_{i\in L\left(
n,T\right) }{\Greekmath 0123} _{i}^{\prime AN} \\
& =\frac{3}{2k_{n}}\sum_{i\in L\left( n,T\right) }\Big[\big(S(
\widehat{C}_{i}^{n},\widehat{C}_{i+k_{n}}^{n})-S(\widehat{C}_{i}^{^{\prime
}n},\widehat{C}_{i+k_{n}}^{^{\prime }n})\big)-\frac{2}{k_{n}}\big(U(\widehat{
C}_{i}^{n})-U(\widehat{C}_{i}^{^{\prime }n})\big)\Big], \\
& \sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{LIN}-\sum_{i\in L\left(
n,T\right) }{\Greekmath 0123} _{i}^{\prime LIN} \\
& =\frac{3}{2k_{n}}\sum_{i\in L\left( n,T\right) }\Big[\big(R(
\widehat{C}_{i}^{n},\widehat{C}_{i+k_{n}}^{n})-R(\widehat{C}_{i}^{^{\prime
}n},\widehat{C}_{i+k_{n}}^{^{\prime }n})\big)-\frac{2}{k_{n}}\big(U(\widehat{
C}_{i}^{n})-U(\widehat{C}_{i}^{^{\prime }n})\big)\Big].
\end{align*}
Since $H$ and $G$ are three times continuously differentiable with bounded
derivatives, the functions $R$ and $S$ are continuously differentiable and
satisfy
\begin{eqnarray}
\Vert \partial J(x,y)\Vert &\leq &K\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for}J\in
\{S,R\}, \\
\Vert \partial U(x)\Vert &\leq &K,
\end{eqnarray}
where $\partial J$ (respectively, $\partial U$) is a vector that collects
the first order partial derivatives of the function $J$ (respectively, $U$)
with respect to all the elements of $(x,y)$ (respectively, $x$). Using the
Taylor expansion, ((ref)) and ((ref)), it holds
that, for $J\in \{S,R\}$,
\begin{align*}
|J(\widehat{C}_{i}^{n},\widehat{C}_{i+k_{n}}^{n})-J(\widehat{C}
_{i}^{^{\prime }n},\widehat{C}_{i+k_{n}}^{^{\prime }n})|& \leq K(\Vert
\widehat{C}_{i}^{n}-\widehat{C}_{i}^{^{\prime }n}\Vert +\Vert \widehat{C}
_{i+k_{n}}^{n}-\widehat{C}_{i+k_{n}}^{^{\prime }n}\Vert ) \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and} \\
|U(\widehat{C}_{i}^{n})-U(\widehat{C}_{i}^{^{\prime }n})|& \leq K(\Vert
\widehat{C}_{i}^{n}-\widehat{C}_{i}^{^{\prime }n}\Vert ).
\end{align*}
By equation ((ref)), the following condition is
sufficient for Theorem (ref) to hold:
\begin{equation*}
(2-r){\Greekmath 0124} -\frac{3}{4}\geq 0.
\end{equation*}
The above condition follows from our assumptions of Theorem (ref).
Using the fact that $0<{\Greekmath 0124} <\frac{1}{2}$, we can see that Theorem (ref) holds when $3/4(2-r)\leq {\Greekmath 0124} <\frac{1}{2}$, which
completes the proof.
\subsection{Proof of Theorem (ref)}
Note that we have
\begin{align*}
& \sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\prime LIN}-\sum_{i\in
L\left( n,T\right) }{\Greekmath 0123} _{i}^{\left( A\right) }=\frac{3}{2k_{n}}
\sum_{g,h,a,b=1}^{d}\sum_{i\in L\left( n,T\right) }{\Greekmath 0120} _{i}^{n}(g,h,a,b), \\
& \sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\prime AN}-\sum_{i\in
L\left( n,T\right) }{\Greekmath 0123} _{i}^{\left( A\right) }=\frac{3}{2k_{n}}
\sum_{i\in L\left( n,T\right) }\Big({\Greekmath 011F} _{i}^{n}-\sum_{g,h,a,b=1}^{d}\big(
\partial _{gh}H\partial _{ab}G\big)(C_{i}^{n}){\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115}
_{i}^{n,ab}\Big),
\end{align*}
with
\begin{align*}
& {\Greekmath 0120} _{i}^{n}(g,h,a,b)=\Big(\big(\partial _{gh}H\partial _{ab}G\big)(
\widehat{C}_{i}^{^{\prime }n})-\big(\partial _{gh}H\partial _{ab}G\big)
(C_{i}^{n})\Big){\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115} _{i}^{n,ab}, \\
& {\Greekmath 011F} _{i}^{n}=\Big(H(\widehat{C}_{i+k_{n}}^{^{\prime }n})-H(\widehat{C}
_{i}^{^{\prime }n})\Big)\Big(G(\widehat{C}_{i+k_{n}}^{^{\prime }n})-G(
\widehat{C}_{i}^{^{\prime }n})\Big).
\end{align*}
By Taylor expansion, we have
\begin{align*}
& \big(\partial _{gh}S\partial _{ab}G\big)(\widehat{C}_{i}^{^{\prime }n})-
\big(\partial _{gh}S\partial _{ab}G\big)(C_{i}^{n})=\sum_{x,y=1}^{d}\Big(
\partial _{xy,gh}^{2}S\partial _{ab}G+\partial _{xy,ab}^{2}G\partial _{gh}S
\Big)(C_{i}^{n}){\Greekmath 0117} _{i}^{n,xy} \\
& +\frac{1}{2}\sum_{j,k,x,y=1}^{d}\Big(\partial _{jk,xy,gh}^{3}S\partial
_{ab}G+\partial _{xy,gh}^{2}S\partial _{jk,ab}^{2}G+\partial
_{jk,xy,ab}^{3}G\partial _{gh}S+\partial _{xy,ab}^{2}G\partial _{jk,gh}^{2}S
\Big)(\widetilde{c}_{i}^{n}){\Greekmath 0117} _{i}^{n,xy}{\Greekmath 0117} _{i}^{n,jk}
\end{align*}
and
\begin{eqnarray*}
&&S(\widehat{C}_{i+k_{n}}^{^{\prime }n})-S(\widehat{C}_{i}^{^{\prime
}n})=\sum_{gh}\partial _{gh}S(C_{i}^{n}){\Greekmath 0115}
_{i}^{n,gh}+\sum_{j,k,g,h}\partial _{jk,gh}^{2}S(C_{i}^{n}){\Greekmath 0115}
_{i}^{n,gh}{\Greekmath 0117} _{i}^{n,jk} \\
&&+\frac{1}{2}\sum_{x,y,g,h}\partial _{xy,gh}^{2}S(C_{i}^{n}){\Greekmath 0115}
_{i}^{n,gh}{\Greekmath 0115} _{i}^{n,xy}+\frac{1}{2}\sum_{x,y,j,k,g,h}\partial
_{xy,jk,gh}^{3}S(CC_{i}^{n,S}){\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0117} _{i}^{n,xy}{\Greekmath 0117}
_{i}^{n,jk} \\
&&+\frac{1}{6}\sum_{j,k,x,y,g,h}\partial
_{jk,xy,gh}^{3}S(C_{i}^{n,S}){\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115}
_{i}^{n,xy},
\end{eqnarray*}
for $S\in \{H,G\}$, $\widetilde{c}_{i}^{n}={\Greekmath 0119} C_{i}^{n}+(1-{\Greekmath 0119} )\widehat{C}
_{i}^{^{\prime }n}$, $C_{i}^{n,S}={\Greekmath 0119} _{S}\widehat{C}_{i}^{^{\prime
}n}+(1-{\Greekmath 0119} _{S})\widehat{C}_{i+k_{n}}^{^{\prime }n}$, $CC_{i}^{n,S}={\Greekmath 0116}
_{S}C_{i}^{n}+(1-{\Greekmath 0116} _{S})\widehat{C}_{i}^{^{\prime }n}$ for ${\Greekmath 0119} ,{\Greekmath 0119}
_{H},{\Greekmath 0116} _{H},{\Greekmath 0119} _{G},{\Greekmath 0116} _{G}\in \lbrack 0,1]$. Although $\widetilde{c}
_{i}^{n}$ and ${\Greekmath 0119} $ depend on $g,h,a,$ and $b$, we do not emphasize this in
our notation to simplify the exposition.\newline
By (4.10) in jacodrosenbaum13 we have
\begin{equation}
\mathbb{E}\Big(\Big\|{\Greekmath 010B} _{i}^{n}\Big\|^{q}\Big|\mathcal{F}_{(i-1)\Delta_n}\Big)
\leq K_{q}\Delta _{n}^{q}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for all}q\geq 0
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}\mathbb{E}\Big(\Big|\sum_{j=0}^{k_{n}-1}
{\Greekmath 010B} _{i+j}^{n}\Big|^{q}\big|\mathcal{F}_{(i-1)\Delta_n}\Big)\leq K_{q}\Delta
_{n}^{q}k_{n}^{q/2}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for}q\geq 2.
\end{equation}
Combining ((ref)), ((ref)), ((ref)) with $Z=C$
and the H\"{o}lder inequality yields for $q\geq 2$, for $i\in L\left(
n,T\right) $
\begin{equation}
\mathbb{E}\Big(\Big\|{\Greekmath 0117} _{i}^{n}\Big\|^{q}\Big|\mathcal{F}_{(i-1)\Delta_n}\Big)\leq
K_{q}\Delta ^{q/4},\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}\mathbb{E}\Big(\Big\|
{\Greekmath 0115} _{i}^{n}\Big\|^{q}\Big|\mathcal{F}_{(i-1)\Delta_n}\Big)\leq K_{q}\Delta
^{q/4}.
\end{equation}
The bound in the first equation of ((ref)) is tighter than that in
(4.11) of jacodrosenbaum-sqrtn due to the absence of volatility
jumps. This tighter bound will be useful later in deriving the asymptotic
distribution for the approximated estimator. By the boundedness of $C_{t}$
and the derivatives of $H$ and $G$,
\begin{equation}
\Big|\big(\partial _{jk,xy,ab}^{3}G\partial _{gh}H+\partial
_{xy,gh}^{2}H\partial _{jk,ab}^{2}G\big)(\widetilde{c}_{i}^{n}){\Greekmath 0117}
_{i}^{n,xy}{\Greekmath 0117} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115} _{i}^{n,ab}\Big|\leq
K\Vert {\Greekmath 0117} _{i}^{n}\Vert ^{2}\Vert {\Greekmath 0115} _{i}^{n}\Vert ^{2}.
\end{equation}
Using the Taylor expansion, we have
\begin{align*}
& {\Greekmath 011F} _{i}^{n}-\sum_{g,h,a,b}(\partial _{gh}H\partial
_{ab}G)(C_{i}^{n}){\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115} _{i}^{n,ab}= \\
& \sum_{g,h,a,b,j,k}(\partial _{gh}H\partial _{jk,xy}^{2}G+\partial
_{gh}G\partial _{jk,xy}^{2}H)(C_{i}^{n})({\Greekmath 0115} _{i}^{n,gh}+\frac{1}{2}{\Greekmath 0117}
_{i}^{n,gh}){\Greekmath 0115} _{i}^{n,ab}{\Greekmath 0115} _{i}^{n,jk}+{\Greekmath 0127} _{i}^{n},\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{ \
and} \\
& \sum_{g,h,a,b}\big(\partial _{gh}H\partial _{ab}G\big)(\widehat{C}
_{i}^{^{\prime }n})-\big(\partial _{gh}H\partial _{ab}G\big)(C_{i}^{n})= \\
& \sum_{g,h,a,b,x,y}(\partial _{gh}H\partial _{ab,xy}^{2}G+\partial
_{ab}G\partial _{gh,xy}^{2}G)(C_{i}^{n})({\Greekmath 0117} _{i}^{n,xy}){\Greekmath 0115}
_{i}^{n,gh}{\Greekmath 0115} _{i}^{n,ab}+{\Greekmath 010E} _{i}^{n}
\end{align*}
with $\mathbb{E}(|{\Greekmath 0127} _{i}^{n}|\big|\mathcal{F}_{i}^{n})\leq K\Delta
_{n} $ and $\mathbb{E}(|{\Greekmath 010E} _{i}^{n}|\big|\mathcal{F}_{i}^{n})\leq
K\Delta _{n} $ which follow by the Cauchy-Schwartz inequality together with
equation ((ref)). Given that $k_{n}={\Greekmath 0112} (\Delta _{n})^{-1/2}$,
the previous inequalities imply
\begin{equation*}
\frac{3\Delta _{n}^{-1/4}}{2k_{n}}\sum_{i\in L\left( n,T\right) }{\Greekmath 0127}
_{i}^{n}\overset{\mathbb{P}}{\Longrightarrow }0\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}\frac{3\Delta _{n}^{-1/4}}{2k_{n}}\sum_{i\in L\left( n,T\right) }{\Greekmath 010E}
_{i}^{n}\overset{\mathbb{P}}{\Longrightarrow }0.
\end{equation*}
Therefore, it suffices to show that
\begin{equation}
\frac{3\Delta _{n}^{-1/4}}{2k_{n}}\sum_{i\in L\left( n,T\right)
}\sum_{g,h,a,b,j,k}(\partial _{gh}H\partial _{jk,ab}^{2}G+\partial
_{gh}H\partial _{jk,ab}^{2}G)(C_{i}^{n}){\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115}
_{i}^{n,ab}{\Greekmath 0115} _{i}^{n,jk}\overset{\mathbb{P}}{\longrightarrow }0,
\end{equation}
\begin{equation}
\frac{3\Delta _{n}^{-1/4}}{2k_{n}}\sum_{i\in L\left( n,T\right)
}\sum_{g,h,a,b,j,k}(\partial _{gh}H\partial _{jk,ab}^{2}G+\partial
_{gh}H\partial _{jk,ab}^{2}G)(C_{i}^{n}){\Greekmath 0117} _{i}^{n,gh}{\Greekmath 0115}
_{i}^{n,ab}{\Greekmath 0115} _{i}^{n,jk}\overset{\mathbb{P}}{\longrightarrow }0.
\end{equation}
These results hold by the bounds in Lemma (ref).
\subsection{Proof of Theorem (ref)}
In Section (ref), to simplify the notational burden, we
adopt the following strategy. Instead of studying $\sum_{i\in L\left(
n,T\right) }{\Greekmath 0123} _{i}^{\left( A\right) }$, we work with all indices $i$
, i.e., $\sum_{i=k_{n}+1}^{[T/\Delta _{n}]-3k_{n}+1}{\Greekmath 0123} _{i}^{\left(
A\right) }$, together with the assumption that there are no volatility
jumps. The difference between the two quantities is $o_{p}\left( \Delta
_{n}^{1/4}\right) $ because in the absence of volatility jumps, ${\Greekmath 0123}
_{i}^{\left( A\right) }$ satisfies the bound in equation ((ref)).
Recall the decomposition from from (ref),
\begin{equation}
{\Greekmath 0123} _{i}^{\left( A\right) }
=
{\Greekmath 0123} _{i}^{\left( A1\right)}
-
{\Greekmath 0123} _{i}^{\left( A2\right) }.
\end{equation}
Given the boundedness of the derivatives of $H$ and $G$ and the fact that $
k_{n}={\Greekmath 0112} (\Delta _{n})^{-1/2}$, by Theorem 2.2 in jacodrosenbaum-sqrtn we have
\begin{equation*}
\frac{1}{\sqrt{\Delta _{n}}}\Bigg(\sum_{i=k_{n}+1}^{[T/\Delta
_{n}]-3k_{n}+1}{\Greekmath 0123} _{i}^{\left( A2\right) }-\frac{3}{{\Greekmath 0112} ^{2}}
\sum_{g,h,a,b=1}^{d}\int_{0}^{T}\big(\partial _{gh}H\partial _{ab}G\big)
(C_{t})(C_{t}^{ga}C_{t}^{hb}+C_{t}^{gb}C_{t}^{ha})dt\Bigg)=O_{p}(1),
\end{equation*}
which yields
\begin{equation*}
\frac{1}{\Delta _{n}^{1/4}}\Bigg(\sum_{i=k_{n}+1}^{[T/\Delta
_{n}]-3k_{n}+1}{\Greekmath 0123} _{i}^{\left( A2\right) }-\frac{3}{{\Greekmath 0112} ^{2}}
\sum_{g,h,a,b=1}^{d}\int_{0}^{T}\big(\partial _{gh}H\partial _{ab}G\big)
(C_{t})(C_{t}^{ga}C_{t}^{hb}+C_{t}^{gb}C_{t}^{ha})dt\Bigg)\overset{\mathbb{P}
}{\longrightarrow }0.
\end{equation*}
Using the multivariate quantities defined in Section (ref), we
can show that the following decompositions hold:
\begin{align*}
\widehat{C}_{i}^{^{\prime }n}& =C_{i-1}^{n}+\frac{1}{k_{n}}
\sum_{j=0}^{k_{n}-1}\sum_{u=1}^{2}\overline{{\Greekmath 0122} }(u)_{j}^{n}{\Greekmath 0110}
(u)_{i+j}^{n},\widehat{C}_{i+k_{n}}^{^{\prime }n}-\widehat{C}
_{i}^{^{\prime }n}=\frac{1}{k_{n}}\sum_{j=0}^{2k_{n}-1}\sum_{u=1}^{2}
{\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0110} (u)_{i+j}^{n}, \\
{\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115} _{i}^{n,ab}& =\frac{1}{k_{n}^{2}}
\sum_{u=1}^{2}\sum_{v=1}^{2}\Bigg(\sum_{j=0}^{2k_{n}-1}{\Greekmath 0122}
(u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}{\Greekmath 0110} (u)_{i+j}^{n,gh}{\Greekmath 0110}
(v)_{i+j}^{n,ab} \\
& +\sum_{j=0}^{2k_{n}-2}\sum_{q=j+1}^{2k_{n}-1}{\Greekmath 0122}
(u)_{j}^{n}{\Greekmath 0122} (v)_{q}^{n}{\Greekmath 0110} (u)_{i+j}^{n,gh}{\Greekmath 0110}
(v)_{i+q}^{n,ab}+\sum_{j=1}^{2k_{n}-1}\sum_{q=0}^{j-1}{\Greekmath 0122}
(u)_{j}^{n}{\Greekmath 0122} (v)_{q}^{n}{\Greekmath 0110} (u)_{i+j}^{n,gh}{\Greekmath 0110}
(v)_{i+q}^{n,ab}\Bigg).
\end{align*}
Changing the order of the summation in the last term yields
\begin{align*}
{\Greekmath 0115} _{i}^{n,gh}{\Greekmath 0115} _{i}^{n,ab}& =\frac{1}{k_{n}^{2}}
\sum_{u=1}^{2}\sum_{v=1}^{2}\Bigg(\sum_{j=0}^{2k_{n}-1}{\Greekmath 0122}
(u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}{\Greekmath 0110} (u)_{i+j}^{n,gh}{\Greekmath 0110}
(v)_{i+j}^{n,ab} \\
& +\sum_{j=0}^{2k_{n}-2}\sum_{q=j+1}^{2k_{n}-1}{\Greekmath 0122}
(u)_{j}^{n}{\Greekmath 0122} (v)_{q}^{n}{\Greekmath 0110} (u)_{i+j}^{n,gh}{\Greekmath 0110}
(v)_{i+q}^{n,ab}+\sum_{j=0}^{2k_{n}-2}\sum_{q=j+1}^{2k_{n}-1}{\Greekmath 0122}
(v)_{j}^{n}{\Greekmath 0122} (u)_{q}^{n}{\Greekmath 0110} (v)_{i+j}^{n,ab}{\Greekmath 0110}
(u)_{i+q}^{n,gh}\Bigg).
\end{align*}
Therefore, we can further rewrite $\sum_{i=k_{n}+1}^{[T/\Delta
_{n}]-3k_{n}+1}{\Greekmath 0123} _{i}^{\left( A1\right) }$ as
\begin{align*}
& \sum_{i=k_{n}+1}^{[T/\Delta _{n}]-3k_{n}+1}{\Greekmath 0123} _{i}^{\left(
A1\right) }=\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\left( A11\right)
}+\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\left( A12\right)
}+\sum_{i\in L\left( n,T\right) }{\Greekmath 0123} _{i}^{\left( A13\right) },\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{
with} \\
& \sum_{i=k_{n}+1}^{[T/\Delta _{n}]-3k_{n}+1}{\Greekmath 0123} _{i}^{\left(
A1w\right) }=\sum_{g,h,a,b=1}^{d}\sum_{u,v=1}^{2}\widehat{A1w}
(H,gh,u;G,ab,v)_{T}^{n},w=1,2,3,
\end{align*}
where
\begin{align*}
& \widehat{A11}(H,gh,u;G,ab,v)_{T}^{n}=\frac{3}{2k_{n}^{3}}
\sum_{i=k_{n}+1}^{[T/\Delta _{n}]-3k_{n}+1}\sum_{j=0}^{2k_{n}-1}(\partial
_{gh}H\partial _{ab}G)(C_{i-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122}
(v)_{j}^{n}{\Greekmath 0110} (u)_{i+j}^{n,gh}{\Greekmath 0110} (v)_{i+j}^{n,ab}, \\
& \widehat{A12}(H,gh,u;G,ab,v)_{T}^{n}=\frac{3}{2k_{n}^{3}}
\sum_{i=k_{n}+1}^{[T/\Delta
_{n}]-3k_{n}+1}\sum_{j=0}^{2k_{n}-2}\sum_{q=j+1}^{2k_{n}-1}(\partial
_{gh}H\partial _{ab}G)(C_{i-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122}
(v)_{q}^{n}{\Greekmath 0110} (u)_{i+j}^{n,gh}{\Greekmath 0110} (v)_{i+q}^{n,ab}, \\
& \widehat{A13}(H,gh,u;G,ab,v)_{T}^{n}=\frac{3}{2k_{n}^{3}}
\sum_{i=k_{n}+1}^{[T/\Delta
_{n}]-3k_{n}+1}\sum_{j=0}^{2k_{n}-2}\sum_{q=j+1}^{2k_{n}-1}(\partial
_{gh}H\partial _{ab}G)(C_{i-1}^{n}){\Greekmath 0122} (v)_{j}^{n}{\Greekmath 0122}
(u)_{q}^{n}{\Greekmath 0110} (v)_{i+j}^{n,ab}{\Greekmath 0110} (u)_{i+q}^{n,gh},
\end{align*}
where we clearly have $\widehat{A13}(H,gh,u;G,ab,v)_{T}^{n}=\widehat{A12}
(G,ab,v;H,gh,u)_{T}^{n}.$ By a change of the order of the summation,
\begin{align*}
\widehat{A11}(H,gh,u;G,ab,v)_{T}^{n}& =\frac{3}{2k_{n}^{3}}
\sum_{i=1}^{[T/\Delta _{n}]}\sum_{j=0\vee (i+2k_{n}-1-[T/\Delta
_{n}])}^{(2k_{n}-1)\wedge (i-1)}(\partial _{gh}H\partial _{ab}G) \\
& \times (C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}{\Greekmath 0110}
(u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}, \\
\widehat{A12}(H,gh,u;G,ab,v)_{T}^{n}& =\frac{3}{2k_{n}^{3}}
\sum_{i=2}^{[T/\Delta _{n}]}\sum_{m=1}^{(i-1)\wedge (2k_{n}-1)}\sum_{j=0\vee
(i+2k_{n}-1-m-[T/\Delta _{n}])}^{(2k_{n}-m-1)\wedge (i-m-1)}(\partial
_{gh}H\partial _{ab}G)(C_{i-1-j-m}^{n}) \\
& \times {\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j+m}^{n}{\Greekmath 0110}
_{gh}(u)_{i-m}^{n}{\Greekmath 0110} _{ab}(v)_{i}^{n}.
\end{align*}
Now, set
\begin{align*}
\widetilde{A11}(H,gh,u;G,ab,v)_{T}^{n}& =\frac{3}{2k_{n}^{3}}
\sum_{i=3k_{n}}^{[T/\Delta _{n}]-k_{n}}\sum_{j=0}^{2k_{n}-1}(\partial
_{gh}H\partial _{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122}
(v)_{j}^{n}{\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}, \\
\widetilde{A12}(H,gh,u;G,ab,v)_{T}^{n}& =\frac{3}{2k_{n}^{3}}
\sum_{i=3k_{n}}^{[T/\Delta _{n}]-k_{n}}\sum_{m=1}^{(i-1)\wedge
(2k_{n}-1)}\sum_{j=0}^{(2k_{n}-m-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1-m}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j+m}^{n} \\
& \times {\Greekmath 0110} _{gh}(u)_{i-m}^{n}{\Greekmath 0110} _{ab}(v)_{i}^{n}.
\end{align*}
We show below that the following results hold:
\begin{equation}
\frac{1}{\Delta _{n}^{1/4}}\Big(\widehat{A1w}(H,gh,u;G,ab,v)_{T}^{n}-
\widetilde{A1w}(H,gh,u;G,ab,v)_{T}^{n}\Big)\overset{\mathbb{P}}{
\longrightarrow }0
\end{equation}
\begin{equation}
\frac{1}{\Delta _{n}^{1/4}}\Big(\widetilde{A1w}(H,gh,u;G,ab,v)_{T}^{n}-
\overline{A1w}(H,gh,u;G,ab,v)_{T}^{n}\Big)\overset{\mathbb{P}}{
\longrightarrow }0
\end{equation}
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for all} $(H,gh,u,G,ab,v)~\relax\protect\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}~w=1,2.$
\subsubsection{Proof of Equation ((ref)) for $w=1$}
To prove this result, first, notice that the ${\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110}
(v)_{i}^{n,ab}$ are scaled by random variables rather that constant real
numbers. Next, observe that we can write
\begin{align*}
& \widehat{A11}-\widetilde{A11}=\widetilde{\widehat{A11}}(1)+\widetilde{
\widehat{A11}}(2)+\widetilde{\widehat{A11}}(3)\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{with} \\
& \widetilde{\widehat{A11}}(1)=\sum_{i=1}^{(2k_{n}-1)\wedge \lbrack T/\Delta
_{n}]}\Bigg(\frac{3}{2k_{n}^{3}}\sum_{j=0\vee (i+2k_{n}-1-[T/\Delta
_{n}])}^{(2k_{n}-1)\wedge (i-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}\Bigg)
{\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}, \\
& \widetilde{\widehat{A11}}(2)=\sum_{i=[T/\Delta _{n}]-2k_{n}+2}^{[T/\Delta
_{n}]}\frac{3}{2k_{n}^{3}}\Bigg(\sum_{j=0\vee (i+2k_{n}-1-[T/\Delta
_{n}])}^{(2k_{n}-1)\wedge (i-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n} \\
& -\sum_{j=0}^{(2k_{n}-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}\Bigg)
{\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}, \\
& \widetilde{\widehat{A11}}(3)=\sum_{i=2k_{n}}^{[T/\Delta _{n}]-2k_{n}+1}
\frac{3}{2k_{n}^{3}}\Bigg(\sum_{j=0\vee (i+2k_{n}-1-[T/\Delta
_{n}])}^{(2k_{n}-1)\wedge (i-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n} \\
& -\sum_{j=0}^{(2k_{n}-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}\Bigg)
{\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}.
\end{align*}
It is easy to see that $\widetilde{\widehat{A12}}(3)=0$. Using equation ((ref)) with $Z=c$ and equation ((ref)), we obtain
\begin{equation}
\mathbb{E}(\Vert {\Greekmath 0110} (1)_{i}^{n}\Vert ^{q}|\mathcal{F}_{i-1}^{n})\leq
K_{q},\mathbb{E}(\Vert {\Greekmath 0110} (2)_{i}^{n}\Vert ^{q}|\mathcal{F}
_{i-1}^{n})\leq K_{q}\Delta _{n}^{q/2}.
\end{equation}
By the boundedness of the derivatives of $H$ and $G$, the random
quantities $\Big(\frac{3}{2k_{n}^{3}}\sum_{j=0\vee (i+2k_{n}-1-[T/\Delta
_{n}])}^{(2k_{n}-1)\wedge (i-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}\Big)$
and\newline
$\frac{3}{2k_{n}^{3}}\sum_{j=0}^{(2k_{n}-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}$ are $
\mathcal{F}_{i-1}^{n}-$ measurable and are bounded by $\widetilde{{\Greekmath 0115} }
_{u,v}^{n}$ defined as
\begin{equation*}
\widetilde{{\Greekmath 0115} }_{u,v}^{n}=
\begin{cases}
K & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(2,2) \\
K/k_{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,2),(2,1) \\
K/k_{n}^{2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,1).
\end{cases}
\end{equation*}
Similarly, the quantity
\begin{equation*}
\frac{3}{2k_{n}^{3}}\Bigg(\sum_{j=0\vee (i+2k_{n}-1-[T/\Delta
_{n}])}^{(2k_{n}-1)\wedge (i-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}-\sum_{j=0}^{(2k_{n}-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}\Bigg),
\end{equation*}
is $\mathcal{F}_{i-1}^{n}-$ measurable and bounded by $2\widetilde{{\Greekmath 0115} }
_{u,v}^{n}$. Note also that, by equation ((ref)) and the Cauchy
Schwartz inequality, we have
\begin{align*}
\mathbb{E}(|{\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}|\big|\mathcal{F}
_{i-1}^{n})& \leq \mathbb{E}(\Vert {\Greekmath 0110} (u)_{i}^{n}\Vert ^{2}|\mathcal{F}
_{i-1}^{n})^{1/2}\mathbb{E}(\Vert {\Greekmath 0110} (v)_{i}^{n}\Vert ^{2}|\mathcal{F}
_{i-1}^{n})^{1/2} \\
& \leq
\begin{cases}
K\Delta _{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(2,2) \\
K\Delta _{n}^{1/2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,2),(2,1) \\
K & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,1).
\end{cases}
\end{align*}
The above bounds, together with the fact that $k_{n}={\Greekmath 0112} \Delta
_{n}^{-1/2}$, imply $\mathbb{E}(|\widetilde{\widehat{A11}}(1)|)\leq K\Delta
_{n}^{1/2}$ and $\mathbb{E}(|\widetilde{\widehat{A11}}(2)|)\leq K\Delta
_{n}^{1/2}$ for all $(u,v)$. These two results together imply $\widetilde{
\widehat{A11}}(1)=o(\Delta _{n}^{-1/4})$ and $\widetilde{\widehat{A11}}
(2)=o(\Delta _{n}^{-1/4})$, which yields the result.
\subsubsection{Proof of Equation ((ref)) for $w=2$}
First, observe that $\widehat{A12}-\widetilde{A12}=\widetilde{\widehat{A12}}
(1)+\widetilde{\widehat{A12}}(2)$, with
\begin{align*}
& \widetilde{\widehat{A12}}(1)=\sum_{i=2}^{(2k_{n}-1)\wedge \lbrack T/\Delta
_{n}]}\Bigg(\sum_{m=1}^{(i-1)}\frac{3}{2k_{n}^{3}}\Big(\sum_{j=0\vee
(i+2k_{n}-1-m-[T/\Delta _{n}])}^{(2k_{n}-m-1)\wedge (i-m-1)}(\partial
_{gh}H\partial _{ab}G)(C_{i-1-j-m}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122}
(v)_{j+m}^{n}\Big) \\
& \times {\Greekmath 0110} _{gh}(u)_{i-m}^{n}\Bigg){\Greekmath 0110} _{ab}(v)_{i}^{n},
\\
& \widetilde{\widehat{A12}}(2)=\sum_{i=[T/\Delta _{n}]-2k_{n}+2}^{[T/\Delta
_{n}]}\Bigg(\sum_{m=1}^{(i-1)\wedge (2k_{n}-1)}\Big(\frac{3}{2k_{n}^{3}}
\sum_{j=0\vee (i+2k_{n}-1-m-[T/\Delta _{n}])}^{(2k_{n}-m-1)\wedge
(i-m-1)}(\partial _{gh}H\partial _{ab}G)(C_{i-1-j-m}^{n}){\Greekmath 0122}
(u)_{j}^{n} \\
& \times {\Greekmath 0122} (v)_{j+m}^{n}\Big)
-\sum_{j=0}^{(2k_{n}-m-1)}(\partial _{gh}H\partial
_{ab}G)(C_{i-1-j-m}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j+m}^{n}\Big)
{\Greekmath 0110} _{gh}(u)_{i-m}^{n}\Bigg){\Greekmath 0110} _{ab}(v)_{i}^{n}.
\end{align*}
Notice that the quantity
\begin{equation*}
{\Greekmath 0114} _{i}^{m,n}=\frac{3}{2k_{n}^{3}}\Big(\sum_{j=0\vee
(i+2k_{n}-1-m-[T/\Delta _{n}])}^{(2k_{n}-m-1)\wedge (i-m-1)}(\partial
_{gh}H\partial _{ab}G)(C_{i-1-j-m}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122}
(v)_{j+m}^{n}\Big)
\end{equation*}
is $\mathcal{F}_{i-m-1}^{n}$ measurable and bounded by $\widetilde{{\Greekmath 0115} }
_{u,v}^{n}$. Let
\begin{equation*}
{\Greekmath 0114} _{i}^{n}=\sum_{m=1}^{(i-1)}\frac{3}{2k_{n}^{3}}\Big(\sum_{j=0\vee
(i+2k_{n}-1-m-[T/\Delta _{n}])}^{(2k_{n}-m-1)\wedge (i-m-1)}(\partial
_{gh}H\partial _{ab}G)(C_{i-1-j-m}^{n}){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122}
(v)_{j+m}^{n}\Big){\Greekmath 0110} _{gh}(u)_{i-m}^{n}.
\end{equation*}
It follows that ${\Greekmath 0114} _{i}^{n}$ is $\mathcal{F}_{i-1}^{n}$-measurable and
we have
\begin{eqnarray*}
\mathbb{E}(|{\Greekmath 0114} _{i}^{m,n}|^{z}\big|\mathcal{F}_{0}) &\leq &(\widetilde{
{\Greekmath 0115} }_{u,v}^{n})^{z}, \\
|\mathbb{E}({\Greekmath 0110} (u)_{i-m}^{n}|\mathcal{F}_{i-m-1})| &\leq &
\begin{cases}
K\sqrt{\Delta _{n}} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}u=1 \\
K\Delta _{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}u=2
\end{cases}
, \\
\mathbb{E}(\Vert {\Greekmath 0110} (u)_{i-m}^{n}\Vert ^{z}|\mathcal{F}_{i-m-1}) &\leq &
\begin{cases}
K_{z} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}u=1 \\
K_{z}\Delta _{n}^{z/2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}u=2
\end{cases}
.
\end{eqnarray*}
Using Lemma (ref), we deduce that for $z\geq 2$,
\begin{equation*}
\mathbb{E}(|{\Greekmath 0114} _{i}^{n}|^{z})\leq
\begin{cases}
K_{z}(\widetilde{{\Greekmath 0115} }_{u,v}^{n})^{z}k_{n}^{z/2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}
u=1 \\
K_{z}(\widetilde{{\Greekmath 0115} }_{u,v}^{n})^{z}/k_{n}^{z/2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}
u=2
\end{cases}
\leq
\begin{cases}
K_{z}/k_{n}^{-3z/2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}v=1 \\
K_{z}k_{n}^{-z/2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}v=2
\end{cases}
.
\end{equation*}
Using the above result, we obtain $\frac{1}{\Delta _{n}^{1/4}}\widetilde{
\widehat{A12}}(1)\overset{\mathbb{P}}{\Rightarrow }0$. A similar argument
yields $\frac{1}{\Delta _{n}^{1/4}}\widetilde{\widehat{A12}}(2)\overset{
\mathbb{P}}{\Rightarrow }0$, which completes the proof of the equation ((ref)) for $w=2$.
\subsubsection{Proof of Equation ((ref)) for $w=1$}
Define
\begin{equation*}
\Theta (u,v)_{0}^{(C),i,n}=\frac{3}{2k_{n}^{3}}
\sum_{j=0}^{2k_{n}-1}\Big((\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n})-(\partial _{gh}H\partial _{ab}G)(C_{i-2k_{n}}^{n})
\Big){\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j}^{n}.
\end{equation*}
By Taylor expansion, boundedness of the derivatives of $H$ and $G$,
and using ((ref)) with $Z=c$, we have
\begin{align*}
& \Big|\mathbb{E}\Big((\partial _{gh}H\partial
_{ab}G)(C_{i-j-1}^{n})-(\partial _{gh}H\partial _{ab}G)(C_{i-2k_{n}}^{n})
\big|\mathcal{F}_{i-2k_{n}}^{n}\Big)\Big|\leq K(k_{n}\Delta _{n})\leq K\sqrt{
\Delta _{n}} \\
& \mathbb{E}(|(\partial _{gh}H\partial _{ab}G)(C_{i-j-1}^{n})-(\partial
_{gh}H\partial _{ab}G)(C_{i-2k_{n}}^{n})|^{q}|\mathcal{F}_{i-2k_{n}}^{n})|
\leq K(k_{n}\Delta _{n})^{q/2}\leq K\Delta _{n}^{q/4},
\end{align*}
for $q\geq 2$ and for $j=0,\ldots ,2k_{n}-1$. Next, observe that $\Theta
(u,v)_{0}^{(C),i,n}$ is $\mathcal{F}_{i-1}^{n}$ -measurable and satisfies $
|\Theta (u,v)_{0}^{(C),i,n}|\leq \widetilde{{\Greekmath 0115} }_{u,v}^{n}$, $|\mathbb{E
}\Big(\Theta (u,v)_{0}^{(C),i,n}|\mathcal{F}_{i-2k_{n}}^{n}\Big)|\leq
K\Delta _{n}^{1/2}\widetilde{{\Greekmath 0115} }_{u,v}^{n}$ and $\mathbb{E}\Big(
|\Theta (u,v)_{0}^{(C),i,n}|^{q}\big|\mathcal{F}_{i-2k_{n}}^{n}\Big)\leq
K_{q}\Delta _{n}^{q/4}(\widetilde{{\Greekmath 0115} }_{u,v}^{n})^{q}$ where the latter
follows from the H\"{o}lder inequality. We aim to prove that
\begin{equation*}
\widehat{E}=\frac{1}{\Delta _{n}^{1/4}}\Bigg[\sum_{i=2k_{n}}^{[T/\Delta
_{n}]}\Theta (u,v)_{0}^{(C),i,n}{\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}
\Bigg]
\end{equation*}
converges to zero in probability for any $H$, $G$, $g$, $h$, $a$, and $b$
with $u,v=1,2$.
\newline
To show this result, we first introduce the following quantities:
\begin{align*}
& \widehat{E}(1)=\frac{1}{\Delta _{n}^{1/4}}\Bigg[\sum_{i=3k_{n}}^{[T/\Delta
_{n}]-k_{n}}\Theta (u,v)_{0}^{(C),i,n}\mathbb{E}({\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110}
(v)_{i}^{n,ab}|\mathcal{F}_{i-1}^{n})\Bigg] \\
& \widehat{E}(2)=\frac{1}{\Delta _{n}^{1/4}}\Bigg[\sum_{i=3k_{n}}^{[T/\Delta
_{n}]-k_{n}}\Theta (u,v)_{0}^{(C),i,n}\big({\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110}
(v)_{i}^{n,ab}-\mathbb{E}({\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}|\mathcal{F
}_{i-1}^{n})\big)\Bigg],
\end{align*}
with $\widehat{E}=\widehat{E}(1)+\widehat{E}(2)$. By Cauchy-Schwartz
inequality, we have
\begin{equation*}
\mathbb{E}(|{\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}|^{q})\leq (\widehat{
{\Greekmath 0115} }_{u,v}^{n})^{q/2},\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{where}\widehat{{\Greekmath 0115} }
_{u,v}^{n}=
\begin{cases}
K & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,1) \\
K\Delta _{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,2),(2,1) \\
K\Delta _{n}^{2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(2,2)
\end{cases}
\end{equation*}
Since ${\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}$ is $\mathcal{F}_{i}^{n}$
-measurable,\newline
the martingale property of ${\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}-\mathbb{
E}({\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}|\mathcal{F}_{i-1}^{n})$ implies,
for all $(u,v)$,
\begin{equation*}
\mathbb{E}(|\widehat{E}(2)|^{2})\leq K\Delta _{n}^{-3/2}(\Delta _{n}^{1/4}
\widetilde{{\Greekmath 0115} }_{u,v}^{n})^{2}\widehat{{\Greekmath 0115} }_{u,v}^{n}\leq K\Delta
_{n}.
\end{equation*}
The latter inequality implies $\widehat{E}(2)\overset{\mathbb{P}}{
\Rightarrow }0$ for all $(u,v)$. It remains to show that $\widehat{E}(1)
\overset{\mathbb{P}}{\Rightarrow }0$.\newline
Here, we recall some bounds under Assumption (ref),
\begin{align}
& |\mathbb{E}({\Greekmath 0110} (1)_{i}^{n,gh}{\Greekmath 0110} (2)_{i}^{n,ab}|\mathcal{F}
_{i-1}^{n})|\leq K\Delta _{n}, \\
& |\mathbb{E}({\Greekmath 0110} (1)_{i}^{n,gh}{\Greekmath 0110} (1)_{i}^{n,ab}|\mathcal{F}
_{i-1}^{n})-\big(C_{i-1}^{n,ga}C_{i-1}^{n,hb}+C_{i-1}^{n,gb}C_{i-1}^{n,ha}
\big)|\leq K\Delta _{n}^{1/2}, \\
& |\mathbb{E}({\Greekmath 0110} (2)_{i}^{n,gh}{\Greekmath 0110} (2)_{i}^{n,ab}|\mathcal{F}_{i-1}^{n}-
\overline{C}_{i-1}^{n,gh,ab}\Delta _{n})|\leq K\Delta _{n}^{3/2}(\sqrt{
\Delta _{n}}+{\Greekmath 0111} _{i}^{n}).
\end{align}
\textbf{Case} $(u,v)\in \{(1,2),(2,1)\}$. By equation ((ref)) we have
\begin{equation*}
\mathbb{E}(|\widehat{E}(1)|)\leq K\frac{T}{\Delta _{n}}\frac{1}{\Delta
_{n}^{1/4}}(\Delta _{n}^{1/4}\widetilde{{\Greekmath 0115} }_{u,v}^{n}\Delta _{n})\leq
K\Delta _{n}^{1/2}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{so}\widehat{E}(1)\overset{
\mathbb{P}}{\Rightarrow }0.
\end{equation*}
\textbf{Case} $(u,v)\in \{(1,1),(2,2)\}$. Set
\begin{align*}
\widehat{E}^{\prime }(1)& =\frac{1}{\Delta _{n}^{1/4}}\Bigg[
\sum_{i=3k_{n}}^{[T/\Delta _{n}]-k_{n}}\Theta
(u,v)_{0}^{(C),i,n}V_{i-2k_{n}}^{n}\Bigg] \\
\widehat{E}^{\prime \prime }(1)& =\frac{1}{\Delta _{n}^{1/4}}\Bigg[
\sum_{i=3k_{n}}^{[T/\Delta _{n}]-k_{n}}\Theta (u,v)_{0}^{(C),i,n}\big(
V_{i-1}^{n}-V_{i-2k_{n}}^{n}\big)\Bigg] \\
\widehat{E}^{\prime \prime \prime }(1)& =\frac{1}{\Delta _{n}^{1/4}}\Bigg[
\sum_{i=3k_{n}}^{[T/\Delta _{n}]-k_{n}}\Theta (u,v)_{0}^{(C),i,n}\Big(
\mathbb{E}({\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}|\mathcal{F}
_{i-1}^{n})-V_{i-1}^{n}\Big)\Bigg]
\end{align*}
where
\begin{equation*}
V_{i-1}^{n}=
\begin{cases}
C_{i-1}^{n,ga}C_{i-1}^{n,hb}+C_{i-1}^{n,gb}C_{i-1}^{n,ha} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(2,2) \\
\overline{C}_{i-1}^{n,gh,ab}\Delta _{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,1)
\\
0 & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{otherwise}
\end{cases}
\end{equation*}
Note that we have $\widehat{E}(1)=\widehat{E}^{\prime }(1)+\widehat{E}
^{\prime \prime }(1)+\widehat{E}^{\prime \prime \prime }(1)$. Using
equations ((ref)) and ((ref)), it can be shown that
\begin{equation*}
\mathbb{E}(|\widehat{E}^{\prime \prime \prime }(1)|)\leq
\begin{cases}
K\frac{1}{\Delta _{n}^{5/4}}(\Delta _{n}^{1/4}\widetilde{{\Greekmath 0115} }
_{u,v}^{n})\Delta _{n}^{1/2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,1) \\
K\frac{1}{\Delta _{n}^{5/4}}(\Delta _{n}^{1/4}\widetilde{{\Greekmath 0115} }
_{u,v}^{n})\Delta _{n}^{3/2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(2,2)
\end{cases}
\leq K\Delta _{n}^{1/2}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{in all cases.}
\end{equation*}
Next, we prove
$\widehat{E}^{\prime }(1)\overset{\mathbb{P}}{\Rightarrow }0$. To this end,
write
\begin{equation*}
\widehat{E}^{\prime }(1)=\frac{1}{\Delta _{n}^{1/4}}\Bigg[
\sum_{i=1}^{[T/\Delta _{n}]-2k_{n}+1}\Theta
(u,v)_{0}^{(C),i-1+2k_{n},n}V_{i-1}^{n}\Bigg].
\end{equation*}
Using the $\mathcal{F}_{i+2k_{n}-2}^{n}$-measurability of the last sum, we
are able to show
\begin{align*}
\frac{1}{\Delta _{n}^{1/4}}\Bigg[\sum_{i=k_{n}+1}^{[T/\Delta _{n}]-3k_{n}+1}|
\mathbb{E}(\Theta (u,v)_{0}^{(C),i-1+2k_{n},n}V_{i-1}^{n}|\mathcal{F}
_{i-1}^{n})|\Bigg]& \overset{\mathbb{P}}{\Rightarrow }0\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}
\\
\frac{2k_{n}-2}{\Delta _{n}^{1/2}}\Bigg[\sum_{i=k_{n}+1}^{[T/\Delta
_{n}]-3k_{n}+1}\mathbb{E}\Big(|\Theta
(u,v)_{0}^{(C),i-1+2k_{n},n}V_{i-1}^{n})|^{2}\Big)\Bigg]& \Rightarrow 0.
\end{align*}
The first result readily follows from the inequality
\begin{equation*}
|\mathbb{E}(\Theta (u,v)_{0}^{(C),i-1+2k_{n},n}V_{i-1}^{n}|\mathcal{F}
_{i-1}^{n})|\leq
\begin{cases}
K\Delta _{n}^{1/2}\widetilde{{\Greekmath 0115} }_{u,v}^{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}
(u,v)=(1,1) \\
K\Delta _{n}^{1/2}\widetilde{{\Greekmath 0115} }_{u,v}^{n}\Delta _{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}
(u,v)=(2,2)
\end{cases}
\leq K\Delta _{n}^{3/2}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{in all cases,}
\end{equation*}
while the second is a direct consequence of
\begin{equation*}
\mathbb{E}(|\Theta (u,v)_{0}^{(C),i-1+2k_{n},n}V_{i-1}^{n}|^{2})\leq
\begin{cases}
K\Delta _{n}^{1/2}(\widetilde{{\Greekmath 0115} }_{u,v}^{n})^{2} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(1,1) \\
K\Delta _{n}^{1/2}(\widetilde{{\Greekmath 0115} }_{u,v}^{n})^{2}\Delta _{n}^{2} &
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(2,2)
\end{cases}
\leq K\Delta _{n}^{5/2}\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{in all cases.}
\end{equation*}
Finally, to prove that $\widehat{E}^{\prime \prime }(1)\overset{\mathbb{P}}{
\Longrightarrow }0$, we use the fact that
\begin{align*}
\mathbb{E}(|\Theta (u,v)_{0}^{(C),i,n}\big(V_{i-1}^{n}-V_{i-2k_{n}}^{n}\big)
|)& \leq \mathbb{E}(|\Theta (u,v)_{0}^{(C),i,n}|^{2})^{1/2}\mathbb{E}
(|V_{i-1}^{n}-V_{i-2k_{n}}^{n}|^{2})^{1/2} \\
& \leq
\begin{cases}
K\Delta _{n}^{1/2}\widetilde{{\Greekmath 0115} }_{u,v}^{n} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}
(u,v)=(1,1) \\
K\Delta _{n}^{1/4}\widetilde{{\Greekmath 0115} }_{u,v}^{n}\Delta _{n}\Delta _{n}^{1/4}
& \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}(u,v)=(2,2)
\end{cases}
,
\end{align*}
which follows from the Cauchy-Schwartz inequality and earlier bounds. In
particular, successive conditioning together with Assumption (ref)
imply that for $(u,v)=(1,1)$ and $(2,2)$,\newline
$\mathbb{E}(|V_{i-1}^{n}-V_{i-2k_{n}}^{n}|^{2})\leq \Delta _{n}^{1/2}$.
\subsubsection{Proof of Equation ((ref)) for $w=2$}
Our aim here is to show that
\begin{align*}
& \widehat{E}(2)=\frac{1}{\Delta _{n}^{1/4}}\sum_{i=3k_{n}}^{[T/\Delta
_{n}]-k_{n}}\Bigg(\sum_{m=1}^{2k_{n}-1}\Big(\frac{3}{2k_{n}^{3}}
\sum_{j=0}^{2k_{n}-m-1}\big[(\partial _{gh}H\partial
_{ab}G)(C_{i-j-m-1}^{n})-(\partial _{gh}H\partial _{ab}G)(C_{i-2k_{n}}^{n})
\big]{\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122} (v)_{j+m}^{n}\Big)\times \\
& {\Greekmath 0110} (u)_{i-m}^{n,gh}\Bigg){\Greekmath 0110} (v)_{i}^{n,ab}\overset{\mathbb{P}}{
\Longrightarrow }0.
\end{align*}
For this purpose, we introduce some new notation. For any $0\leq m\leq
2k_{n}-1$, set
\begin{align*}
& \Theta (u,v)_{m}^{(C),i,n}=\frac{3}{2k_{n}^{3}}\sum_{j=0}^{2k_{n}-m-1}\big[
(\partial _{gh}H\partial _{ab}G)(C_{i-j-m-1}^{n})-(\partial _{gh}H\partial
_{ab}G)(C_{i-2k_{n}}^{n})\big]{\Greekmath 0122} (u)_{j}^{n}{\Greekmath 0122}
(v)_{j+m}^{n} \\
& {\Greekmath 011A} (u,v)^{(C),i,n,gh}=\sum_{m=1}^{2k_{n}-1}\Theta
(u,v)_{m}^{(C),i,n}{\Greekmath 0110} (u)_{i-m}^{n,gh}.
\end{align*}
It is easy to see that $\Theta (u,v)_{m}^{(C),i,n}$ is $\mathcal{F}
_{i-m-1}^{n}$ measurable and satisfies, by H\"{o}lder inequality,
\begin{equation*}
|\Theta (u,v)_{m}^{(C),i,n}|\leq \widetilde{{\Greekmath 0115} }_{u,v}^{n}
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}\mathbb{E}\Big(|\Theta (u,v)_{m}^{(C),i,n}|^{q}\big|
\mathcal{F}_{i-2k_{n}}^{n}\Big)\leq K_{q}\Delta _{n}^{q/4}(\widetilde{
{\Greekmath 0115} }_{u,v}^{n})^{q}.
\end{equation*}
Lemma (ref) implies that for $q\geq 2$,
\begin{equation}
\mathbb{E}(|{\Greekmath 011A} (u,v)^{(C),i,n,gh}|^{q})\leq
\begin{cases}
K_{q}(\Delta _{n}^{1/4}\widetilde{{\Greekmath 0115} }_{u,v}^{n})^{q}k_{n}^{q/2} &
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}u=1 \\
K_{q}(\Delta _{n}^{1/4}\widetilde{{\Greekmath 0115} }_{u,v}^{n})^{q}/k_{n}^{q/2} &
\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}u=2
\end{cases}
\leq
\begin{cases}
K_{q}/k_{n}^{2q} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}v=1 \\
K_{q}k_{n}^{q} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if}v=2
\end{cases}
.
\end{equation}
Set
\begin{align*}
& \widehat{E}^{\prime }(2)=\frac{1}{\Delta _{n}^{1/4}}\sum_{i=3k_{n}}^{[T/
\Delta _{n}]-k_{n}}{\Greekmath 011A} (u,v)^{(C),i,n,gh}\mathbb{E}({\Greekmath 0110} (v)_{i}^{n,ab}|
\mathcal{F}_{i-1}^{n}), \\
& \widehat{E}^{\prime \prime }(2)=\frac{1}{\Delta _{n}^{1/4}}
\sum_{i=3k_{n}}^{[T/\Delta _{n}]-k_{n}}{\Greekmath 011A} (u,v)^{(C),i,n,gh}({\Greekmath 0110}
(v)_{i}^{n,ab}-\mathbb{E}({\Greekmath 0110} (v)_{i}^{n,ab}|\mathcal{F}_{i-1}^{n})).
\end{align*}
The martingale increments property implies $\mathbb{E}(|\widehat{E}^{\prime
\prime }(2)|^{2})\leq K\Delta _{n}^{1/2}$ in all the cases, which in turn
implies $\widehat{E}^{\prime \prime }(2)\overset{\mathbb{P}}{\Longrightarrow
}0$. Next, using the bounds on ${\Greekmath 011A} (u,v)^{(C),i,n,gh}$, we obtain that $
\widehat{E}^{\prime }(2)\overset{\mathbb{P}}{\Longrightarrow }0$.
We refer to jacodrosenbaum-sqrtn for the proofs of Lemma (ref) and Lemma (ref).
\subsection{Proof of Lemma (ref)}
Set
\begin{align*}
& {\Greekmath 0118}_i^n={\Greekmath 0127}^n_{i-1}{\Greekmath 0110}_i^n,
{\Greekmath 0118}_i^{^{\prime }n}=\mathbb{E}({\Greekmath 0118}_i|\mathcal{F}_{i-1}^n)=\mathbb{E}
({\Greekmath 0127}^n_{i-1}{\Greekmath 0110}_i^n|\mathcal{F}_{i-1}^n)={\Greekmath 0127}^n_{i-1}\mathbb{E}
({\Greekmath 0110}_i^n|\mathcal{F}_{i-1}^n),\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}
{\Greekmath 0118}_i^{^{\prime \prime }n}={\Greekmath 0118}_i^{n}-{\Greekmath 0118}_i^{^{\prime }n}.
\end{align*}
Given that $\|\mathbb{E}({\Greekmath 0110}_{i}^n|\mathcal{F}_{i-1}^n)\| \leq L^{\prime}$
, we have $\|{\Greekmath 0118}_i^{^{\prime }n}\|\leq L^{\prime} |{\Greekmath 0127}^n_{i-1}|$. By the
convexity of the function $x^q$, which holds for $q\geq 2$, we have
\begin{align*}
\|\sum_{j=1}^{2k_n-1}{\Greekmath 0118}_{i+j}^{n}\|^q \leq K\Big(\|\sum_{j=1}^{2k_n-1}
{\Greekmath 0118}_{i+j}^{^{\prime }n}\|^q+\|\sum_{j=1}^{2k_n-1}{\Greekmath 0118}_{i+j}^{^{\prime \prime
}n}\|^q\Big).
\end{align*}
Therefore, on the one hand we have
\begin{align*}
&\|\sum_{j=1}^{2k_n-1}{\Greekmath 0118}_{i+j}^{^{\prime }n}\|^q \leq Kk_n^{q-1}
\sum_{j=1}^{2k_n-1}\|{\Greekmath 0118}_{i+j}^{^{\prime }n}\|^q \leq Kk_n^{q-1}L^{\prime q
}\sum_{j=1}^{2k_n-1} |{\Greekmath 0127}_{i+j-1}^n|^q,
\end{align*}
which by $\mathbb{E}\Big(\|{\Greekmath 0127}_{i+j-1}^n\|^q \Big|\mathcal{F}_{i-1}^n
\Big) \leq L^q$, satisfies
\begin{align*}
&\mathbb{E}(\|\sum_{j=1}^{2k_n-1}{\Greekmath 0118}_{i+j}^{^{\prime }n}\|^q | \mathcal{F}
_{i-1}^n)\leq KL^{\prime q}k_n^{q-1} \sum_{j=1}^{2k_n-1} \mathbb{E}
(|{\Greekmath 0127}_{i+j-1}^n|^q|\mathcal{F}_{i-1}^n)\leq KL^{\prime q}k_n^qL^q.
\end{align*}
On the other hand, we have $\mathbb{E}(\|{\Greekmath 0118}_{i+j}^{^{\prime \prime }n}\|^q
| \mathcal{F}_{i-1}^n)\leq \mathbb{E}(\|{\Greekmath 0118}_{i+j}^{n}\|^q | \mathcal{F}
_{i-1}^n) \leq L_qL^q$ and $\mathbb{E}({\Greekmath 0118}_{i+j}^{^{\prime \prime }n} |
\mathcal{F}_{i-1}^n)=0$, where the first inequality is a consequence of $
\mathbb{E}(\|{\Greekmath 0118}_{i+j}^{^{\prime }n}\|^q | \mathcal{F}_{i-1}^n)\leq \mathbb{E
}(\|{\Greekmath 0118}_{i+j}^{n}\|^q | \mathcal{F}_{i-1}^n) \leq L_qL^q$, which follows
from the Jensen's inequality and the law of iterated expectations. Hence, by
Lemma B.2 of yacjacod14 we have
\begin{align*}
\mathbb{E}(\|\sum_{j=1}^{2k_n-1}{\Greekmath 0118}_{i+j}^{^{\prime \prime }n}\|^q |
\mathcal{F}_{i-1}^n) \leq K_qL^qL_qk_n^{q/2}.
\end{align*}
To see the latter, we first prove that the required condition $\mathbb{E}
(\|{\Greekmath 0118}_{i}^{n}\|^q | \mathcal{F}_{i-1}^n) \leq L_qL^q$) in the Lemma B.2 of
yacjacod14 can be replaced by $\mathbb{E}(\|{\Greekmath 0118}_{i+j}^{n}\|^q |
\mathcal{F}_{i-1}^n) \leq L_qL^q$) for $1\leq j \leq 2k_n-1$ without
altering the result.\newline
\subsection{Proof of Lemma (ref)}
We use $i\in L\left( n,T\right) $ throughout the proof of Lemma
(ref). We use the terminology \textquotedblleft successive
conditioning" to refer to either of the following two equalities,
\begin{eqnarray*}
x_{1}y_{1}-x_{0}y_{0}
&=&x_{0}(y_{1}-y_{0})+y_{0}(x_{1}-x_{0})+(x_{1}-x_{0})(y_{1}-y_{0}), \\
x_{1}y_{1}z_{1}-x_{0}y_{0}z_{0}
&=&x_{0}y_{0}(z_{1}-z_{0})+x_{0}z_{0}(y_{1}-y_{0})+y_{0}z_{0}(x_{1}-x_{0})+x_{0}(y_{0}-y_{1})(z_{0}-z_{1})
\\
&&+y_{0}(x_{0}-x_{1})(z_{0}-z_{1})+z_{0}(x_{0}-x_{1})(y_{0}-y_{1})+(x_{1}-x_{0})(y_{1}-y_{0})(z_{1}-z_{0}),
\end{eqnarray*}
which hold for any real numbers $x_{0},y_{0},z_{0},x_{1},y_{1},$ and $z_{1}$
. \newline
To prove Lemma (ref), we first note that ${\Greekmath 0115}
_{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}$ is $\mathcal{F}_{i+2k_{n}}^{n}$-measurable.
Therefore, by the law of iterated expectations, we have
\begin{equation*}
\mathbb{E}\Big({\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}{\Greekmath 0115}
_{i+2k_{n}}^{n,gh}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}|\mathcal{F}_{i}^{n}\Big)=
\mathbb{E}\Big({\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}\mathbb{E}\big({\Greekmath 0115}
_{i+2k_{n}}^{n,gh}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}|\mathcal{F}_{i+2k_{n}}^{n}\big)|
\mathcal{F}_{i}^{n}\Big).
\end{equation*}
By equation (3.27) in jacodrosenbaum-sqrtn, we have
\begin{align*}
& |\mathbb{E}({\Greekmath 0115} _{i+2k_{n}}^{n,gh}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}|\mathcal{F
}_{i+2k_{n}}^{n})-\frac{2}{k_{n}}
(C_{i+2k_{n}}^{n,ga}C_{i+2k_{n}}^{n,hb}+C_{i+2k_{n}}^{n,gb}C_{i+2k_{n}}^{n,ha})-
\frac{2k_{n}\Delta _{n}}{3}\overline{C}_{i+2k_{n}}^{n,gh,ab}| \\
& \leq K\sqrt{\Delta _{n}}(\Delta _{n}^{1/8}+{\Greekmath 0111}
_{i+2k_{n},2k_{n}}^{n}), \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and} \\
& |\mathbb{E}({\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}|\mathcal{F}_{i}^{n})-
\frac{2}{k_{n}}(C_{i}^{n,jl}C_{i}^{n,km}+C_{i}^{n,jm}C_{i}^{n,kl})-\frac{
2k_{n}\Delta _{n}}{3}\overline{C}_{i}^{n,jk,lm}|\leq K\sqrt{\Delta _{n}}
(\Delta _{n}^{1/8}+{\Greekmath 0111} _{i,2k_{n}}^{n}).
\end{align*}
From the above, it follows that
\begin{align*}
& |\mathbb{E}\Big({\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}\Big[\mathbb{E}
({\Greekmath 0115} _{i+2k_{n}}^{n,gh}{\Greekmath 0115} _{i+2k_{n}}^{n,ab}\Big|\mathcal{F}
_{i+2k_{n}}^{n})-\frac{2}{k_{n}}
(C_{i+2k_{n}}^{n,ga}C_{i+2k_{n}}^{n,hb}+C_{i+2k_{n}}^{n,gb}C_{i+2k_{n}}^{n,ha})-
\frac{2k_{n}\Delta _{n}}{3}\overline{C}_{i+2k_{n}}^{n,gh,ab}\Big]\Bigg|
\mathcal{F}_{i}^{n}\Big)| \\
& \leq \sqrt{\Delta _{n}}\mathbb{E}(|{\Greekmath 0115}
_{i}^{n,jk}||{\Greekmath 0115} _{i}^{n,lm}|(\Delta _{n}^{1/8}+{\Greekmath 0111}
_{i+2k_{n},2k_{n}}^{n})|\Big|\mathcal{F}_{i}^{n})\leq K\sqrt{\Delta _{n}}
\Delta _{n}^{1/8}\mathbb{E}(|{\Greekmath 0115} _{i}^{n,jk}||{\Greekmath 0115} _{i}^{n,lm}|\Big|
\mathcal{F}_{i}^{n}) \\
& +K\sqrt{\Delta _{n}}\mathbb{E}(|{\Greekmath 0115} _{i}^{n,jk}||{\Greekmath 0115}
_{i}^{n,lm}|{\Greekmath 0111} _{i+2k_{n},2k_{n}}^{n}|\Big|\mathcal{F}_{i}^{n})\leq
K\Delta _{n}(\Delta _{n}^{1/8}+{\Greekmath 0111} _{i,4k_{n}}^{n}),
\end{align*}
where the last inequality follows from Lemma (ref). \newline
Now, using equation ((ref)) successively with $Z=C$ and $Z=
\overline{C}$ (recall that the latter holds under Assumption (ref)
), together with the successive conditioning, we also have
\begin{align*}
& |\mathbb{E}\Big({\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}\Big[\frac{2}{k_{n}}
(C_{i+2k_{n}}^{n,ga}C_{i+2k_{n}}^{n,hb}+C_{i+2k_{n}}^{n,gb}C_{i+2k_{n}}^{n,ha})+
\frac{2k_{n}\Delta _{n}}{3}\overline{C}_{i+2k_{n}}^{n,gh,ab}-\frac{2}{k_{n}}
(C_{i}^{n,ga}C_{i}^{n,hb}+C_{i}^{n,gb}C_{i}^{n,ha}) \\
& -\frac{2k_{n}\Delta _{n}}{3}\overline{C}_{i}^{n,gh,ab}\Big]
\Big|\mathcal{F}_{i}^{n}\Big)|\leq K\Delta _{n}\Delta _{n}^{1/4}, \\
& |\mathbb{E}\Big({\Greekmath 0115} _{i}^{n,jk}{\Greekmath 0115} _{i}^{n,lm}\Big[\frac{2}{k_{n}}
(C_{i}^{n,ga}C_{i}^{n,hb}+C_{i}^{n,gb}C_{i}^{n,ha}) \\
& +\frac{2k_{n}\Delta _{n}}{3}\overline{C}_{i}^{n,gh,ab}\Big]-
\Big[\frac{2}{k_{n}}(C_{i}^{n,jl}C_{i}^{n,km}+C_{i}^{n,jm}C_{i}^{n,kl})+
\frac{2k_{n}\Delta _{n}}{3}\overline{C}_{i}^{n,jk,lm}\Big] \\
& \times \Big[\frac{2}{k_{n}}
(C_{i}^{n,ga}C_{i}^{n,hb}+C_{i}^{n,gb}C_{i}^{n,ha})+\frac{2k_{n}\Delta _{n}}{
3}\overline{C}_{i}^{n,gh,ab}\Big]\Big|\mathcal{F}_{i}^{n}\Big)|\leq K\Delta
_{n}(\Delta _{n}^{1/8}+{\Greekmath 0111} _{i,2k_{n}}^{n}).
\end{align*}
The result derives from the last inequality.\newline
\subsection{Proof of Lemma (ref)}
\subsubsection{Proof of Equation ((ref)) in Lemma (ref)}
We start by obtaining some useful bounds for some important quantities.
First, using the second statement in Lemma (ref) applied to $
Z=Y^{\prime }$, we have
\begin{equation}
|\mathbb{E}({\Greekmath 010B}_i^{n,jk}|\mathcal{F}_{i-1}^n)|\leq K\Delta_n^{3/2}(\sqrt{
\Delta_n}+{\Greekmath 0111}^n_{i,1}).
\end{equation}
Second, by repeated application of the Cauchy-Schwartz inequality and making
use of the third and last statements in Lemma (ref) as well as
equation ((ref)) with $Z=C$, it can be shown that
\begin{align}
\Big|\mathbb{E}({\Greekmath 010B}_i^{n,jk}{\Greekmath 010B}_i^{n,lm}&|\mathcal{F}_{i-1}^n)-\Delta_n^2
\Big(C_i^{n,jl}C_i^{n,km}+C_i^{n,jm}C_i^{n,kl}\Big)\Big|\leq K\Delta_n^{5/2}.
\end{align}
Next, by successive conditioning and using the bound in equation ((ref)) for $Z=C$ as well as equations ((ref)) and ((ref)), we have for $0\leq u \leq k_n-1$,
\begin{align}
\Big|\mathbb{E}({\Greekmath 010B}_{i+u}^{n,jk}\big|\mathcal{F}_{i-1}^n)\Big|\leq
K\Delta_n^{3/2}(\sqrt{\Delta_n}+{\Greekmath 0111}^n_{i,u}),
\end{align}
\begin{align}
\Big|\mathbb{E}({\Greekmath 010B}_{i+u}^{n,jk}{\Greekmath 010B}_{i+u}^{n,lm}&|\mathcal{F}
_{i-1}^n)-\Delta_n^2\Big(C_i^{n,jl}C_i^{n,km}+C_i^{n,jm}C_i^{n,kl}\Big)\Big|\leq
K\Delta_n^{5/2}.
\end{align}
To prove equation ((ref)), we first observe that $
{\Greekmath 0117}_i^{n,jk}{\Greekmath 0117}_i^{n,lm}{\Greekmath 0117}_i^{n,gh}$ can be decomposed as
\begin{align*}
&{\Greekmath 0117}_i^{n,jk}{\Greekmath 0117}_i^{n,lm}{\Greekmath 0117}_i^{n,gh}=\frac{1}{k_n^3\Delta_n^3}
\sum_{u=0}^{k_n-1}{\Greekmath 0110}_{i,u}^{n,jk}{\Greekmath 0110}_{i,u}^{n,lm}{\Greekmath 0110}_{i,u}^{n,gh}+
\frac{1}{k_n^3\Delta_n^3}\sum_{u=0}^{k_n-2}\sum_{v=u+1}^{k_n-1}\Big[
{\Greekmath 0110}_{i,u}^{n,jk}{\Greekmath 0110}_{i,v}^{n,lm}{\Greekmath 0110}_{i,v}^{n,gh}+
{\Greekmath 0110}_{i,u}^{n,gh}{\Greekmath 0110}_{i,v}^{n,jk}{\Greekmath 0110}_{i,v}^{n,lm} \\
& +{\Greekmath 0110}_{i,u}^{n,lm}{\Greekmath 0110}_{i,v}^{n,gh}{\Greekmath 0110}_{i,v}^{n,jk}\Big]+
\frac{1}{k_n^3\Delta_n^3}\sum_{u=0}^{k_n-2}\sum_{v=u+1}^{k_n-1}[
{\Greekmath 0110}_{i,u}^{n,jk}{\Greekmath 0110}_{i,u}^{n,lm}{\Greekmath 0110}_{i,v}^{n,gh}+
{\Greekmath 0110}_{i,u}^{n,gh}{\Greekmath 0110}_{i,u}^{n,jk}{\Greekmath 0110}_{i,v}^{n,lm}+{\Greekmath 0110}_{i,u}^{n,lm}
{\Greekmath 0110}_{i,u}^{n,gh}{\Greekmath 0110}_{i,v}^{n,jk}\Big] \\
& +\frac{1}{k_n^3\Delta_n^3}\sum_{u=0}^{k_n-3}
\sum_{v=u+1}^{k_n-2}\sum_{w=v+1}^{k_n-1}\Big[{\Greekmath 0110}_{i,u}^{n,jk}
{\Greekmath 0110}_{i,v}^{n,lm}{\Greekmath 0110}_{i,w}^{n,gh}+{\Greekmath 0110}_{i,u}^{n,jk}{\Greekmath 0110}_{i,v}^{n,gh}
{\Greekmath 0110}_{i,w}^{n,lm}+{\Greekmath 0110}_{i,u}^{n,lm}{\Greekmath 0110}_{i,v}^{n,jk}{\Greekmath 0110}_{i,w}^{n,gh}+
{\Greekmath 0110}_{i,u}^{n,lm}{\Greekmath 0110}_{i,v}^{n,gh}{\Greekmath 0110}_{i,w}^{n,jk} \\
& +{\Greekmath 0110}_{i,u}^{n,gh}{\Greekmath 0110}_{i,v}^{n,lm}{\Greekmath 0110}_{i,w}^{n,jk}+
{\Greekmath 0110}_{i,u}^{n,gh}{\Greekmath 0110}_{i,v}^{n,jk}{\Greekmath 0110}_{i,w}^{n,lm}\Big],
\end{align*}
with ${\Greekmath 0110}_{i,u}^{n}={\Greekmath 010B}_{i+u}^{n}+(C_{i+u}^{n}-C_i^{n})\Delta_n$, which
satisfies $\mathbb{E}(\|{\Greekmath 0110}_{i,u}^n\|^q|\mathcal{F}_{i-1}^n)\leq K\Delta_n^q$
for $q\geq 2$.\newline
Set
\begin{align*}
&{\Greekmath 0118}_i^n(1)=\frac{1}{k_n^3\Delta_n^3}\sum_{u=0}^{k_n-1}{\Greekmath 0110}_{i,u}^{n,jk}
{\Greekmath 0110}_{i,u}^{n,lm}{\Greekmath 0110}_{i,u}^{n,gh},{\Greekmath 0118}_i^n(2)=\frac{1}{
k_n^3\Delta_n^3}\sum_{u=0}^{k_n-2}\sum_{v=u+1}^{k_n-1}{\Greekmath 0110}_{i,u}^{n,jk}
{\Greekmath 0110}_{i,v}^{n,lm}{\Greekmath 0110}_{i,v}^{n,gh} \\
&{\Greekmath 0118}_i^n(3)=\frac{1}{k_n^3\Delta_n^3}\sum_{u=0}^{k_n-2}\sum_{v=u+1}^{k_n-1}
{\Greekmath 0110}_{i,u}^{n,jk}{\Greekmath 0110}_{i,u}^{n,lm}{\Greekmath 0110}_{i,v}^{n,gh} \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}
{\Greekmath 0118}_i^n(4)=\frac{1}{k_n^3\Delta_n^3}\sum_{u=0}^{k_n-3}\sum_{v=u+1}^{k_n-2}
\sum_{w=v+1}^{k_n-1}{\Greekmath 0110}_{i,u}^{n,jk}{\Greekmath 0110}_{i,v}^{n,lm}{\Greekmath 0110}_{i,w}^{n,gh}.
\end{align*}
The following bounds complete the proof of equation ((ref)),
\begin{align}
& |\mathbb{E}({\Greekmath 0118}_i^n(1)|\mathcal{F}_{i-1}^n) |\leq K\Delta_n \\
& |\mathbb{E}({\Greekmath 0118}_i^n(2)|\mathcal{F}_{i-1}^n) |\leq K\Delta_n \\
&|\mathbb{E}({\Greekmath 0118}_i^n(3)|\mathcal{F}_{i-1}^n) |\leq K\Delta_n \\
& |\mathbb{E}({\Greekmath 0118}_i^n(4)|\mathcal{F}_{i-1}^n) |\leq
K\Delta_n^{3/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,k_n}) .
\end{align}
These bounds are proved below.
{\textbf{Proof of Equation ((ref))}}
The result readily follows from an application of the Cauchy Schwartz
inequality coupled with the bound $\mathbb{E}(\|{\Greekmath 0110}^n_{i+u}\|^q|\mathcal{F}
_{i-1}^n)\leq K_q\Delta_n^q$ for $q\geq 2$.
{\textbf{Proof of Equation ((ref))}}
Using the law of iterated expectation, we have, for $u<v$,
\begin{equation}
\mathbb{E}({\Greekmath 0110}_{i+u}^{n,jk}{\Greekmath 0110}_{i+v}^{n,lm}{\Greekmath 0110}_{i+v}^{n,gh}|\mathcal{F}
_{i-1}^n)=\mathbb{E}({\Greekmath 0110}_{i+u}^{n,jk}\mathbb{E}({\Greekmath 0110}_{i+v}^{n,lm}
{\Greekmath 0110}_{i+v}^{n,gh}|\mathcal{F}_{i+u+1}^n)\big|\mathcal{F}_{i-1}^n).
\end{equation}
By successive conditioning, equation ((ref)), and the Cauchy-Schwartz
inequality, we also have
\begin{equation*}
|\mathbb{E}({\Greekmath 0110}_{i,v}^{n,lm}{\Greekmath 0110}_{i,v}^{n,gh}|\mathcal{F}
_{i+u}^n)-
\Delta_n^2(C_{i+u+1}^{n,lg}C_{i+u+1}^{n,mh}+C_{i+u+1}^{n,lh}C_{i+u+1}^{n,mg})
\\
-\Delta_n^2(C_{i+u+1}^{n,gh}-C_{i}^{n,gh})(C_{i+u+1}^{n,lm}-C_{i}^{n,lm})|
\leq K\Delta_n^{5/2}.
\end{equation*}
Given that $\mathbb{E}(|{\Greekmath 0110}_{i+u}^{n,jk}|^q\big|\mathcal{F}_{i-1}^n)\leq
\Delta^q_n$, the approximation error involved in replacing $\mathbb{E}
({\Greekmath 0110}_{i+v}^{n,lm}{\Greekmath 0110}_{i+v}^{n,gh}|\mathcal{F}_{i+u+1}^n)$ by\newline
$
\Delta_n^2(C_{i+u+1}^{n,lg}C_{i+u+1}^{n,mh}+C_{i+u+1}^{n,lh}C_{i+u+1}^{n,mg})+\Delta_n^2(C_{i+u+1}^{n,gh}-C_{i}^{n,gh})(C_{i+u+1}^{n,lm}-C_{i}^{n,lm})
$ in equation ((ref)) is smaller than $\Delta_n^{7/2}$. \newline
We can also easily show that
\begin{equation}
|\mathbb{E}({\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,lm}-C_{i+u}^{n,lm})|\mathcal{F}
_{i-1}^n)|\leq K\Delta_n^{3/2}(\sqrt{\Delta_n}+{\Greekmath 0111}_{i,k_n}^n).
\end{equation}
Since $(C_{i+u}^n-C_i^n)$ is $\mathcal{F}_{i+u}^n$-measurable, we use the
successive conditioning, the Cauchy-Schwartz inequality, equation ((ref)), equation ((ref)), and the fifth statement in Lemma (ref) applied to $Z=c$ to obtain
\begin{eqnarray}
|\mathbb{E}
({\Greekmath 010B}_{i+u}^{n,gh}(C_{i+u}^{n,lm}-C_i^{n,lm})(C_{i+u}^{n,jk}-C_i^{n,jk})|
\mathcal{F}_{i-1}^n)| & \leq & K\Delta_n^{5/2} \notag \\
|\mathbb{E}({\Greekmath 010B}_{i+u}^{n,jk}
{\Greekmath 010B}_{i+u}^{n,lm}(C_{i+u}^{n,gh}-C_i^{n,gh})|\mathcal{F}_{i-1}^n)| & \leq &
K\Delta_n^{5/2} \\
|\mathbb{E}\big(
(C_{i+u}^{n,lm}-C_i^{n,lm})(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+u}^{n,gh}-C_i^{n,gh})
\big)|\mathcal{F}_{i-1}^n)| & \leq & K\Delta_n. \notag
\end{eqnarray}
The following inequalities can be established
using equation ((ref)), the successive conditioning together with
equation ((ref)) for $Z=C$,
\begin{eqnarray*}
\Big|\mathbb{E}
({\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,lg}C_{i+u+1}^{n,mh}+C_{i+u+1}^{n,lh}C_{i+u+1}^{n,mg})|
\mathcal{F}_{i-1}^n)\Big| &\leq & K\Delta_n^{3/2} \\
\Big|\mathbb{E}\Big((C_{i+u}^{n,jk}-C_{i}^{n,jk})\big(
C_{i+u+1}^{n,lg}C_{i+u+1}^{n,mh}+C_{i+u+1}^{n,lh}C_{i+u+1}^{n,mg}\big)|
\mathcal{F}_{i-1}^n\Big)\Big| & \leq & K\Delta_n^{1/2} \\
\Big|\mathbb{E}
({\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,gh}-C_{i}^{n,gh})(C_{i+u+1}^{n,lm}-C_{i}^{n,lm})|
\mathcal{F}_{i-1}^n)\Big| & \leq & K\Delta_n^{3/2}(\sqrt{\Delta_n}
+{\Greekmath 0111}_{i,k_n}^n).
\end{eqnarray*}
The last three inequalities together yield $|\mathbb{E}({\Greekmath 0118}_i^n(2)|\mathcal{F
}_{i-1}^n) |\leq K\Delta_n$.
{\textbf{Proof of Equation ((ref))}}
First, note that, for $u<v$, we have
\begin{equation}
\mathbb{E}({\Greekmath 0110} _{i+u}^{n,jk}{\Greekmath 0110} _{i+u}^{n,lm}{\Greekmath 0110} _{i+v}^{n,gh}|
\mathcal{F}_{i-1}^{n})=\mathbb{E}({\Greekmath 0110} _{i+u}^{n,jk}{\Greekmath 0110} _{i+u}^{n,lm}
\mathbb{E}({\Greekmath 0110} _{i+v}^{n,gh}|\mathcal{F}_{i+u}^{n})\big|\mathcal{F}
_{i-1}^{n}).
\end{equation}
By successive conditioning and equation ((ref)), we have
\begin{equation}
|\mathbb{E}({\Greekmath 010B} _{i+w}^{n,gh}|\mathcal{F}_{i+v}^{n})|\leq K\Delta
_{n}^{3/2}(\sqrt{\Delta _{n}}+{\Greekmath 0111} _{i+v+1,w-v}).
\end{equation}
Using the first statement of Lemma applied to $Z=C$, it can be shown that
\begin{eqnarray*}
&&|\mathbb{E}\big((C_{i+w}^{n,gh}-C_{i+v+1}^{n,gh}))|\mathcal{F}_{i-1}^{n}\big)
-\Delta _{n}(w-v-1)\widetilde{b}_{i+v+1}^{n,gh}| \\
&\leq &K(w-v-1)\Delta _{n}{\Greekmath 0111} _{i+v+1,w-v}\leq K\Delta _{n}^{1/2}{\Greekmath 0111}
_{i+v+1,w-v}.
\end{eqnarray*}
The last two inequalities together imply
\begin{equation}
\Big|\mathbb{E}\Big({\Greekmath 0110} _{i+w}^{n,gh}|\mathcal{F}_{i+v}^{n}\Big)
-(C_{i+v+1}^{n,gh}-C_{i}^{n,gh})\Delta _{n}-\Delta _{n}^{2}(w-v-1)\widetilde{
b}_{i+v+1}^{n,gh}\Big|\leq K\Delta _{n}^{3/2}(\sqrt{\Delta _{n}}+{\Greekmath 0111}
_{i+v+1,w-v}).
\end{equation}
Since $\mathbb{E}(|{\Greekmath 0110} _{i,u}^{n,jk}|^{q}|\mathcal{F}_{i-1}^{n})\leq \Delta
_{n}^{q}$, the error induced by replacing $\mathbb{E}({\Greekmath 0110} _{i+v}^{n,gh}|
\mathcal{F}_{i+u}^{n})$ by $(C_{i+v+1}^{n,gh}-C_{i}^{n,gh})\Delta
_{n}+\Delta _{n}^{2}(w-v-1)\widetilde{b}_{i+v+1}^{n,gh}$ in equation ((ref)) is smaller that $\Delta _{n}^{7/2}$.\newline
Using Cauchy Schwartz inequality, successive conditioning, equation ((ref)), equation ((ref)) for $Z=C$ and the boundedness of $
\widetilde{b}_{t}$ and $C_{t}$ we obtain
\begin{eqnarray*}
\Big|\mathbb{E}\Big({\Greekmath 010B} _{i+u}^{n,jk}{\Greekmath 010B}
_{i+u}^{n,lm}(C_{i+u+1}^{n,jk}-C_{i}^{n,gh})|\mathcal{F}_{i+u-1}^{n}\Big)\Big|
&\leq &K\Delta _{n}^{5/2} \\
\Big|\mathbb{E}\Big({\Greekmath 010B} _{i+u}^{n,jk}{\Greekmath 010B} _{i+u}^{n,lm}\widetilde{b}
_{i+u+1}^{n,gh}|\mathcal{F}_{i+u-1}^{n}\Big)\Big| &\leq &K\Delta _{n}^{2} \\
\Big|\mathbb{E}\Big({\Greekmath 010B}
_{i+u}^{n,jk}(C_{i+u}^{n,lm}-C_{i}^{n,lm})(C_{i+u+1}^{n,gh}-C_{i}^{n,gh})|
\mathcal{F}_{i-1}^{n}\Big)\Big| &\leq &K\Delta _{n}^{1/4}\Delta _{n}^{3/2}(
\sqrt{\Delta _{n}}+{\Greekmath 0111} _{i,k_{n}}^{n}) \\
\Big|\mathbb{E}\Big({\Greekmath 010B} _{i+u}^{n,jk}(C_{i+u}^{n,lm}-C_{i}^{n,lm})
\widetilde{b}_{i+u+1}^{n,gh}|\mathcal{F}_{i-1}^{n}\Big)\Big| &\leq &\Delta
_{n}^{5/4} \\
\Big|\mathbb{E}\Big(
(C_{i+u}^{n,jk}-C_{i}^{n,gh})(C_{i+u}^{n,lm}-C_{i}^{n,lm})\widetilde{b}
_{i+u+1}^{n,gh}|\mathcal{F}_{i-1}^{n}\Big)\Big| &\leq &K\Delta _{n}^{1/2} \\
\Big|\mathbb{E}\Big(
(C_{i+u}^{n,jk}-C_{i}^{n,jk})(C_{i+u}^{n,lm}-C_{i}^{n,lm})(C_{i+u+1}^{n,gh}-C_{i}^{n,gh})|
\mathcal{F}_{i-1}^{n}\Big)\Big| &\leq &K\Delta _{n}.
\end{eqnarray*}
The above inequalities together yield $|\mathbb{E}({\Greekmath 0118} _{i}^{n}(3)|\mathcal{F
}_{i-1}^{n})|\leq K\Delta _{n}$.
{\textbf{Proof of Equation ((ref))}}
We first observe that ${\Greekmath 0118}_i^n(4)$ can be rewritten as
\begin{align*}
{\Greekmath 0118}_i^n(4)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}
\sum_{u=0}^{v-1}{\Greekmath 0110}_{i+u}^{n,jk}{\Greekmath 0110}_{i+v}^{n,lm}{\Greekmath 0110}_{i+w}^{n,gh},
\end{align*}
where
\begin{align*}
&{\Greekmath 0110}_{i+u}^{n,jk}{\Greekmath 0110}_{i+v}^{n,lm}{\Greekmath 0110}_{i+w}^{n,gh}=\Bigg[
{\Greekmath 010B}_{i+u}^{n,jk}{\Greekmath 010B}_{i+v}^{n,lm}{\Greekmath 010B}_{i+w}^{n,gh}+
{\Greekmath 010B}_{i+u}^{n,jk}\Delta_n{\Greekmath 010B}_{i+v}^{n,lm}(C_{i+w}^{n,gh}-C_i^{n,gh})+
{\Greekmath 010B}_{i+u}^{n,jk}\Delta_n(C_{i+v}^{n,lm}-C_i^{n,lm}){\Greekmath 010B}_{i+w}^{n,gh} \\
&+\Delta_n^2
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+v}^{n,lm}-C_i^{n,lm})(C_{i+w}^{n,gh}-C_i^{n,gh})+
\Delta_n(C_{i+u}^{n,jk}-C_i^{n,jk}){\Greekmath 010B}_{i+v}^{n,lm}{\Greekmath 010B}_{i+w}^{n,gh} \\
&+\Delta_n^2(C_{i+u}^{n,jk}-C_i^{n,jk})
{\Greekmath 010B}_{i+v}^{n,lm}(C_{i+w}^{n,gh}-C_i^{n,gh})
+\Delta_n^2(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+v}^{n,lm}-C_i^{n,lm})
{\Greekmath 010B}_{i+w}^{n,gh} \\
&
+\Delta_n^3(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+v}^{n,lm}-C_i^{n,lm})(C_{i+w}^{n,gh}-C_i^{n,gh})
\Bigg].
\end{align*}
Based on the above decomposition, we set
\begin{align*}
{\Greekmath 0118}_i^n(4)=\sum_{j=1}^8 {\Greekmath 011F}(j),
\end{align*}
with ${\Greekmath 011F}(j)$ defined below. We aim to show that $|\mathbb{E}({\Greekmath 011F}(j)\big|
\mathcal{F}_{i-1}^n)|\leq K\Delta_n^{3/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,k_n}^n)$, $
j=1,\ldots,8$.\newline
First, set
\begin{align*}
{\Greekmath 011F}(1)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}
\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}{\Greekmath 010B}_{i+v}^{n,lm}{\Greekmath 010B}_{i+w}^{n,gh}.
\end{align*}
Upon changing the order of the summation, we have
\begin{align*}
&{\Greekmath 011F}(1)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\Big(
\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big){\Greekmath 010B}_{i+v}^{n,lm}
{\Greekmath 010B}_{i+w}^{n,gh}.
\end{align*}
Define also
\begin{align*}
{\Greekmath 011F}^{\prime}(1)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}
\Big(\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big){\Greekmath 010B}_{i+v}^{n,lm}\mathbb{E}
({\Greekmath 010B}_{i+w}^{n,gh}|\mathcal{F}_{i+v}^n).
\end{align*}
Note that $\mathbb{E}({\Greekmath 011F}(1)|\mathcal{F}_{i-1}^n)=\mathbb{E}({\Greekmath 011F}^{\prime}(1)|
\mathcal{F}_{i-1}^n)$.\newline
By Lemma (ref), we have for $q\geq 2$,
\begin{align*}
&\mathbb{E}\Big(\Big\|\sum_{u=0}^{v-1}{\Greekmath 010B}_{i+u}^{n,jk}\Big\|^q \Big|
\mathcal{F}^n_{i-1}\Big)\leq K_q\Delta_n^{3q/4}.
\end{align*}
The Cauchy-Schwartz inequality yields
\begin{align*}
&\mathbb{E}\Bigg(\Big|\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\Big(
\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big){\Greekmath 010B}_{i+v}^{n,lm}\mathbb{E}
({\Greekmath 010B}_{i+w}^{n,gh}|\mathcal{F}_{i+v}^n)\Big|\Bigg|\mathcal{F}_{i-1}^n\Bigg)
\leq Kk_n^2 \Big[\mathbb{E}\Big(\Big|\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}
\Big|^{4}\Big|\mathcal{F}_{i-1}^n\Big)\Big]^{1/4} \\
&\times\Big[\mathbb{E}\Big(\Big|{\Greekmath 010B}_{i+v}^{n,lm}\Big|^{4}\Big|\mathcal{F}
_{i-1}^n\Big)\Big]^{1/4}\times\Big[\mathbb{E}\Big(\Big|\mathbb{E}
({\Greekmath 010B}_{i+w}^{n,gh}|\mathcal{F}_{i+v}^n)\Big|^{2}\Big|\mathcal{F}_{i-1}^n\Big)
\Big]^{1/2}\leq K\Delta_n k_n^2 \Delta_n^{3/4} \Delta_n^{3/2}(\sqrt{\Delta_n}
+{\Greekmath 0111}_{i,k_n}^{n}),
\end{align*}
where the last iteration is obtained using equation ((ref)) as
well as the inequality $(a+b)^{1/2}\leq a^{1/2}+b^{1/2}$, which holds for
positive real numbers $a$ and $b$, and the third statement in Lemma (ref). It follows that
\begin{align*}
|\mathbb{E}\Big({\Greekmath 011F}(1)\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n^{3/4} (\sqrt{
\Delta_n}+{\Greekmath 0111}_{i,k_n}^n).
\end{align*}
Next, we introduce
\begin{align*}
&{\Greekmath 011F}(2)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\Big(
\sum_{u=0}^{v-1}\Delta_n(C_{i+u}^{n,jk}-C_{i}^{n,jk})\Big)
{\Greekmath 010B}_{i+v}^{n,lm}{\Greekmath 010B}_{i+w}^{n,gh}, \\
&{\Greekmath 011F}(3)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\Big(
\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+v}^{n,jk}\Big)
\Delta_n(C_{i+u}^{n,lm}-C_{i}^{n,lm}){\Greekmath 010B}_{i+w}^{n,gh}, \\
&{\Greekmath 011F}(4)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\Big(
\sum_{u=0}^{v-1} \Delta_n(C_{i+u}^{n,jk}-C_{i}^{n,jk})\Big)
\Delta_n(C_{i+u}^{n,lm}-C_{i}^{n,lm}){\Greekmath 010B}_{i+w}^{n,gh}.
\end{align*}
Given that for $q \geq 2$, we have
\begin{align*}
&\mathbb{E}\Big(\Big\|\sum_{u=0}^{v-1}\Delta_n(C_{i+u}^{n,jk}-C_{i}^{n,jk})
\Big\|^q \Big|\mathcal{F}^n_{i-1}\Big)\leq K_q\Delta_n^{3q/4} \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and}
\mathbb{E}(\|C_{i+u}^{n,jk}-C_{i}^{n,jk}\|^q\big|\mathcal{F}_{i-1}^n)\leq K_q
\Delta_n^{q/4}.
\end{align*}
Similar steps to ${\Greekmath 011F}(1)$ lead to
\begin{align*}
|\mathbb{E}({\Greekmath 011F}(2)\big|\mathcal{F}_{i-1}^n)|\leq K\Delta_n^{3/4} (\sqrt{\Delta_n
}+{\Greekmath 0111}_{i,k_n}^n) \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{and} |\mathbb{E}({\Greekmath 011F}(j)\big|\mathcal{F}_{i-1}^n)|\leq
K\Delta_n(\sqrt{\Delta_n}+{\Greekmath 0111}_{i,k_n}^n) \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for} j=3,4.
\end{align*}
Define
\begin{align*}
&{\Greekmath 011F}(5)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\Big(
\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big){\Greekmath 010B}_{i+v}^{n,lm}
\Delta_n(C_{i+w}^{n,gh}-C_i^{n,gh}) \\
&{\Greekmath 011F}^{\prime}(5)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}
\Big(\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big){\Greekmath 010B}_{i+v}^{n,lm}\Delta_n
\mathbb{E}\big((C_{i+w}^{n,gh}-C_i^{n,gh})\big|\mathcal{F}_{i+v}^n) \\
&{\Greekmath 011F}(6)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\Big(
\sum_{u=0}^{v-1} \Delta_n(C_{i+u}^{n,jk}-C_i^{n,jk})\Big){\Greekmath 010B}_{i+v}^{n,lm}
\Delta_n(C_{i+w}^{n,gh}-C_i^{n,gh}) \\
&{\Greekmath 011F}(7)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\Big(
\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big)\Delta_n(C_{i+v}^{n,lm}-C_i^{n,lm})
\Delta_n(C_{i+w}^{n,gh}-C_{i}^{n,gh}),
\end{align*}
where we have $\mathbb{E}({\Greekmath 011F}(5)|\mathcal{F}_{i-1}^n)=\mathbb{E}
({\Greekmath 011F}^{\prime}(5)|\mathcal{F}_{i-1}^n)$. Recalling equation ((ref)),
we further decompose ${\Greekmath 011F}^{\prime}(5)$ as,
\begin{align*}
{\Greekmath 011F}^{\prime}(5)=\sum_{j=1}^{5}{\Greekmath 011F}(5)[j],
\end{align*}
with
\begin{align*}
{\Greekmath 011F}{\prime}(5)[1]=&\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\Big(\sum_{u=0}^{v-1}{\Greekmath 010B}_{i+u}^{n,jk}\Big)
{\Greekmath 010B}_{i+v}^{n,lm}\Big(\mathbb{E}\Big(C_{i+w}^{n,gh}-C_{i}^{n,gh}|\mathcal{F
}_{i+v}^n\Big) \\
&-(C_{i+v+1}^{n,gh}-C_{i}^{n,gh})\Delta_n -\widetilde{b}_{i+v+1}^{n,gh}
\Delta_n^2(w-v-1)\Big) \\
{\Greekmath 011F}{\prime}(5)[2]=&\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\Delta_n(C_{i+v}^{n,gh}-C_{i}^{n,gh})\Big(
\sum_{u=0}^{v-1}{\Greekmath 010B}_{i+u}^{n,jk}\Big){\Greekmath 010B}_{i+v}^{n,lm} \\
{\Greekmath 011F}{\prime}(5)[3]=&\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\Big(\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big)
\Delta_n(C_{i+v+1}^{n,gh}-C_{i+v}^{n,gh}){\Greekmath 010B}_{i+v}^{n,lm} \\
{\Greekmath 011F}{\prime}(5)[4]=&\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\Big(\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big)
\Delta_n^2(w-v-1)(\widetilde{b}_{i+v+1}^{n,gh}-\widetilde{b}
_{i+v}^{n,gh}){\Greekmath 010B}_{i+v}^{n,lm} \\
{\Greekmath 011F}{\prime}(5)[5]=&\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\Delta_n^2(w-v-1)\widetilde{b}_{i+v}^{n,gh}\Big(
\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big){\Greekmath 010B}_{i+v}^{n,lm}.
\end{align*}
Using equations ((ref)), ((ref)), and ((ref)) and following the same strategy proof as for ${\Greekmath 011F}(1)$, it
can be shown that
\begin{align*}
|\mathbb{E}\Big({\Greekmath 011F}{\prime}(5)[j]\big|\mathcal{F}_{i-1}^n\Big)|\leq
K\Delta_n^{3/4} (\sqrt{\Delta_n}+{\Greekmath 0111}_{i,k_n}^n), \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for} j=1,\ldots,5,
\end{align*}
which in turn implies
\begin{align*}
|\mathbb{E}\Big({\Greekmath 011F}(5)\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n^{3/4} (\sqrt{
\Delta_n}+{\Greekmath 0111}_{i,k_n}^n), \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for} j=1,\ldots,5.
\end{align*}
The term ${\Greekmath 011F}(6)$ can be handled similarly to ${\Greekmath 011F}(5)$, hence we conclude
that
\begin{align*}
|\mathbb{E}\Big({\Greekmath 011F}(6)\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n^{3/4} (\sqrt{
\Delta_n}+{\Greekmath 0111}_{i,k_n}^n).
\end{align*}
Next, we set
\begin{align*}
{\Greekmath 011F}(7)=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\Bigg(\sum_{v=0}^{w-1}
\Big(\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big)
\Delta_n(C_{i+v}^{n,lm}-C_i^{n,lm})\Delta_n(C_{i+w}^{n,gh}-C_{i}^{n,gh})
\Bigg).
\end{align*}
Define
\begin{align*}
&{\Greekmath 011F}(7)[1]=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\Bigg(\sum_{v=0}^{w-1}
\Big(\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big)
\Delta_n(C_{i+v}^{n,lm}-C_i^{n,lm})\Delta_n(C_{i+v+1}^{n,gh}-C_{i+v}^{n,gh})
\Bigg) \\
&{\Greekmath 011F}(7)[2]=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\Bigg(\sum_{v=0}^{w-1}
\Big(\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big)
\Delta_n(C_{i+v}^{n,lm}-C_i^{n,lm})\Delta_n(C_{i+v}^{n,gh}-C_i^{n,gh})\Bigg)
\\
&{\Greekmath 011F}(7)[3]=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\Bigg(\sum_{v=0}^{w-1}
\Big(\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big)
\Delta_n(C_{i+v}^{n,lm}-C_i^{n,lm})\Delta_n^2(w-v-1)(\widetilde{b}
_{i+v+1}^{n,gh}-\widetilde{b}_{i+v}^{n,gh})\Bigg) \\
&{\Greekmath 011F}(7)[4]=\frac{1}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}\Bigg(
\sum_{v=0}^{w-1}\Delta_n^2(w-v-1)\widetilde{b}_{i+v}^{n,gh}\Big(
\sum_{u=0}^{v-1} {\Greekmath 010B}_{i+u}^{n,jk}\Big)\Delta_n(C_{i+v}^{n,lm}-C_i^{n,lm})
\Bigg).
\end{align*}
It is easy to see that
\begin{align*}
{\Greekmath 011F}(7)=\sum_{j=1}^4 {\Greekmath 011F}(7)[j].
\end{align*}
Similarly to calculations used for ${\Greekmath 011F}(1)$, it can be shown that
\begin{align*}
|\mathbb{E}({\Greekmath 011F}(7)[j]\big|\mathcal{F}_{i-1}^n)|\leq
K\Delta_n^{1/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,k_n}), \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for} j=1,\ldots,3.
\end{align*}
To handle the remaining term ${\Greekmath 011F}(7)[4]$, we decompose it $
{\Greekmath 011F}(7)[4]=\sum_{j=1}^9 {\Greekmath 011F}(7)[4][j]$, where
\begin{align*}
&{\Greekmath 011F}(7)[4][1]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,lm}-C_{i+u}^{n,lm})(C_{i+u+1}^{n,gh}-C_{i+u}^{n,gh})
\\
&{\Greekmath 011F}(7)[4][2]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
(C_{i+u}^{n,gh}-C_{i}^{n,gh})
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,lm}-C_{i+u}^{n,lm}) \\
&{\Greekmath 011F}^{\prime}(7)[4][2]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}
\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
(C_{i+u}^{n,gh}-C_{i}^{n,gh})\mathbb{E}
({\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,lm}-C_{i+u}^{n,lm})|\mathcal{F}_{i+u-1}^n) \\
&{\Greekmath 011F}(7)[4][3]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
(C_{i+u}^{n,lm}-C_{i}^{n,lm})
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,gh}-C_{i+u}^{n,gh}) \\
&{\Greekmath 011F}(7)[4][4]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
(C_{i+u}^{n,lm}-C_{i}^{n,lm})(C_{i+u}^{n,gh}-C_{i}^{n,gh}){\Greekmath 010B}_{i+u}^{n,jk}
\\
&{\Greekmath 011F}(7)[4][5]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
(C_{i+u}^{n,lm}-C_{i}^{n,lm})
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+v}^{n,gh}-C_{i+u+1}^{n,gh}) \\
&{\Greekmath 011F}^{\prime}(7)[2][5]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}
\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
(C_{i+u}^{n,lm}-C_{i}^{n,lm}){\Greekmath 010B}_{i+u}^{n,jk}\mathbb{E}
((C_{i+v}^{n,gh}-C_{i+u+1}^{n,gh}|\mathcal{F}_{i+u-1}^n) \\
&{\Greekmath 011F}(7)[4][6]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,lm}-C_{i+u}^{n,lm})(C_{i+v}^{n,gh}-C_{i+u+1}^{n,gh})
\\
&{\Greekmath 011F}(7)[4][7]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
(C_{i+u}^{n,gh}-C_{i}^{n,gh})
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+v}^{n,lm}-C_{i+u+1}^{n,lm}) \\
&{\Greekmath 011F}(7)[4][8]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+u+1}^{n,gh}-C_{i+u}^{n,gh})(C_{i+v}^{n,lm}-C_{i+u+1}^{n,lm})
\\
&{\Greekmath 011F}(7)[4][9]=\frac{\Delta_n^2}{(k_n\Delta_n)^3}\sum_{w=2}^{k_n-1}
\sum_{v=0}^{w-1}\sum_{u=0}^{v-1}
{\Greekmath 010B}_{i+u}^{n,jk}(C_{i+v}^{n,lm}-C_{i+u+1}^{n,lm})(C_{i+v}^{n,gh}-C_{i+u+1}^{n,gh}).
\\
\end{align*}
Using arguments similar to those involved for the treatment of ${\Greekmath 011F}(1)$, it
can be shown that
\begin{align*}
|\mathbb{E}({\Greekmath 011F}(7)[4][j]\big|\mathcal{F}_{i-1}^n)|\leq
K\Delta_n^{1/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,k_n}), \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for} j=1,\ldots,8,
\end{align*}
which yields
\begin{align*}
|\mathbb{E}({\Greekmath 011F}(7)\big|\mathcal{F}_{i-1}^n)|\leq
K\Delta_n^{1/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,k_n}).
\end{align*}
Next, define
\begin{align*}
{\Greekmath 011F}(8)=\frac{1}{k_n^3}\sum_{w=2}^{k_n-1}\sum_{v=0}^{w-1}
\sum_{u=0}^{v-1}(C_{i+u}^{n,jk}-C_{i}^{n,jk})(C_{i+v}^{n,lm}-C_{i}^{n,lm})(C_{i+w}^{n,gh}-C_{i}^{n,gh}).
\end{align*}
This term can be further decomposed into six
components. Successive conditioning and existing bounds give
\begin{align*}
&|\mathbb{E}\Big(
(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+v}^{n,lm}-C_{i+u}^{n,lm})(C_{i+w}^{n,gh}-C_{i+v}^{n,gh})
\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n \\
&|\mathbb{E}\Big(
(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+v}^{n,lm}-C_{i+u}^{n,lm})(C_{i+v}^{n,gh}-C_{i+u}^{n,gh})
\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n^{3/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,k_n})
\\
&|\mathbb{E}\Big(
(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+v}^{n,lm}-C_{i+u}^{n,lm})(C_{i+u}^{n,gh}-C_{i}^{n,gh})
\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n \\
&|\mathbb{E}\Big(
(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+u}^{n,lm}-C_{i}^{n,lm})(C_{i+w}^{n,gh}-C_{i+v}^{n,gh})
\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n \\
&|\mathbb{E}\Big(
(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+u}^{n,lm}-C_{i}^{n,lm})(C_{i+v}^{n,gh}-C_{i+u}^{n,gh})
\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n \\
&|\mathbb{E}\Big(
(C_{i+u}^{n,jk}-C_i^{n,jk})(C_{i+u}^{n,lm}-C_{i}^{n,lm})(C_{i+u}^{n,gh}-C_{i}^{n,gh})
\big|\mathcal{F}_{i-1}^n\Big)|\leq K\Delta_n
\end{align*}
These bounds can be used to deduce
\begin{align*}
|\mathbb{E}({\Greekmath 011F}(8)\big|\mathcal{F}_{i-1}^n)|\leq K\Delta_n.
\end{align*}
This completes the proof.
\subsubsection{Proof of Equations ((ref)) and ((ref)) in Lemma (ref)}
Observe that
\begin{align*}
&{\Greekmath 0117}_i^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})=
\frac{1}{k_n\Delta_n}\sum_{u=0}^{k_n-1}
{\Greekmath 0110}_{i,u}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})(C_{i+k_n}^{n,gh}-C_{i}^{n,gh}),
\\
&{\Greekmath 0117}_i^{n,jk}{\Greekmath 0117}_i^{n,lm}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})=\frac{1}{
k_n^2\Delta_n^2} \sum_{u=0}^{k_n-1}{\Greekmath 0110}_{i,u}^{n,jk}
{\Greekmath 0110}_{i,u}^{n,lm}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh}) \\
&+\frac{1}{k_n^2\Delta_n^2} \sum_{u=0}^{k_n-2}\sum_{v=0}^{k_n-1}
{\Greekmath 0110}_{i,u}^{n,jk}{\Greekmath 0110}_{i,v}^{n,lm}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh}) +\frac{1
}{k_n^2\Delta_n^2}\sum_{u=0}^{k_n-2}\sum_{v=0}^{k_n-1}{\Greekmath 0110}_{i,u}^{n,lm}
{\Greekmath 0110}_{i,v}^{n,jk}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh}).
\end{align*}
Hence, equations ((ref)) and ((ref)) can be proved using the
same strategy as for ((ref)).
\subsubsection{Proof of Equations ((ref)) and ((ref)) in Lemma (ref)}
Note that we have
\begin{align*}
&{\Greekmath 0115}_i^{n,jk}{\Greekmath 0115}_i^{n,lm}{\Greekmath 0117}_i^{n,gh}={\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,jk}
{\Greekmath 0117}_{i+k_n}^{n,lm}+{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i}^{n,jk}{\Greekmath 0117}_{i}^{n,lm}-{\Greekmath 0117}_i^{n,gh}
{\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i+k_n}^{n,jk}-{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i+k_n}^{n,jk}
\\
&+{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})-{\Greekmath 0117}_i^{n,gh}
{\Greekmath 0117}_{i}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})+{\Greekmath 0117}_i^{n,gh}
{\Greekmath 0117}_{i+k_n}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk}) \\
&-{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})
+{\Greekmath 0117}_i^{n,gh}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})(C_{i+k_n}^{n,lm}-C_{i}^{n,lm}),
\end{align*}
and
\begin{eqnarray*}
&&{\Greekmath 0115}_i^{n,gh}{\Greekmath 0115}_i^{n,jk}{\Greekmath 0115}_i^{n,lm}={\Greekmath 0117}_{i+k_n}^{n,gh}
{\Greekmath 0117}_{i+k_n}^{n,jk}{\Greekmath 0117}_{i+k_n}^{n,lm}+{\Greekmath 0117}_{i+k_n}^{n,gh}{\Greekmath 0117}_{i}^{n,jk}
{\Greekmath 0117}_{i}^{n,lm}-{\Greekmath 0117}_{i+k_n}^{n,gh}{\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i+k_n}^{n,jk}-
{\Greekmath 0117}_{i+k_n}^{n,gh}{\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i+k_n}^{n,jk} \\
&&+{\Greekmath 0117}_{i+k_n}^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})-
{\Greekmath 0117}_{i+k_n}^{n,gh}{\Greekmath 0117}_{i}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})+
{\Greekmath 0117}_{i+k_n}^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk}) \\
&&-{\Greekmath 0117}_{i+k_n}^{n,gh}{\Greekmath 0117}_{i}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})
+{\Greekmath 0117}_{i+k_n}^{n,gh}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})
\\
&&
-{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,jk}{\Greekmath 0117}_{i+k_n}^{n,lm}-{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i}^{n,jk}
{\Greekmath 0117}_{i}^{n,lm}
+{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i+k_n}^{n,jk}+{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i}^{n,lm}
{\Greekmath 0117}_{i+k_n}^{n,jk} \\
&&-{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})+
{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})-{\Greekmath 0117}_i^{n,gh}
{\Greekmath 0117}_{i+k_n}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk}) \\
&&+{\Greekmath 0117}_i^{n,gh}{\Greekmath 0117}_{i}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})
-{\Greekmath 0117}_i^{n,gh}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})
\\
&& +{\Greekmath 0117}_{i+k_n}^{n,jk}{\Greekmath 0117}_{i+k_n}^{n,lm}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})
+{\Greekmath 0117}_{i}^{n,jk}{\Greekmath 0117}_{i}^{n,lm}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})
-{\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i+k_n}^{n,jk}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh}) \\
&&
-{\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i+k_n}^{n,jk}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})+
{\Greekmath 0117}_{i+k_n}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})
\\
&&-
{\Greekmath 0117}_{i}^{n,jk}(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})
+{\Greekmath 0117}_{i+k_n}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})
\\
&&
-{\Greekmath 0117}_{i}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})(C_{i+k_n}^{n,gh}-C_{i}^{n,gh}) +(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})(C_{i+k_n}^{n,gh}-C_{i}^{n,gh}).
\end{eqnarray*}
From ((ref)), notice that ${\Greekmath 0117}_i^{n}$ is $\mathcal{F}_{i+k_n-1}^n$
-measurable and satisfies $\|\mathbb{E}({\Greekmath 0117}_i^{n}|\mathcal{F}_{i-1}^n)\|\leq
K\Delta_n^{1/2}$.\newline
The law of iterated expectations and existing bounds imply
\begin{eqnarray}
|\mathbb{E}({\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i+k_n}^{n,jk}|\mathcal{F}_{i-1}^n)| & \leq &
K\Delta_n^{3/4}, \notag \\
|\mathbb{E}({\Greekmath 0117}_{i}^{n,lm}{\Greekmath 0117}_{i}^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,jk}|\mathcal{F}_{i-1}^n)|
& \leq & K\Delta_n, \notag \\
|\mathbb{E}({\Greekmath 0117}_{i}^{n,lm}(C_{i+k_n}^{n,gh}-C_{i}^{n,gh}){\Greekmath 0117}_{i+k_n}^{n,jk}|
\mathcal{F}_{i-1}^n)| & \leq & K\Delta_n, \notag \\
|\mathbb{E}({\Greekmath 0117}_{i+k_n}^{n,lm}(C_{i+k_n}^{n,jk}-C_{i}^{n,jk})|\mathcal{F}
_{i-1}^n)| & \leq & K\Delta_n^{3/4}, \notag \\
|\mathbb{E}
((C_{i+k_n}^{n,jk}-C_{i}^{n,jk})(C_{i+k_n}^{n,lm}-C_{i}^{n,lm})(C_{i+k_n}^{n,gh}-C_{i}^{n,gh})|
\mathcal{F}_{i-1}^n)| & \leq & K\Delta_n.
\end{eqnarray}
It can also be readily verified that
\begin{align*}
&|\mathbb{E}({\Greekmath 0117}_{i+k_n}^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,ab}|\mathcal{F}_{i+k_n-1}^n)-
\frac{1}{k_n}
(C_{i+k_n}^{n,ga}C_{i+k_n}^{n,hb}+C_{i+k_n}^{n,gb}C_{i+k_n}^{n,ha})-\frac{
k_n\Delta_n}{3}\overline{C}_{i+k_n}^{n,gh,ab}| \\
&\leq K\sqrt{\Delta_n}(\Delta_n^{1/8}+{\Greekmath 0111}_{i+k_n,k_n}^n).
\end{align*}
Hence, for ${\Greekmath 0127}_i^{n,gh}
\in\{{\Greekmath 0117}_i^{n,gh},C_{i+k_n}^{n,gh}-C_{i}^{n,gh}\}$, which satisfies $\mathbb{
E}(|{\Greekmath 0127}_i^{n,gh}|^q\Big| \mathcal{F}_{i-1}^n) \leq K\Delta_n^{q/4}$ and $
\mathbb{E}({\Greekmath 0127}_i^{n,gh}|\mathcal{F}_{i-1}^n)\leq K\Delta_n^{1/2}$. One can
show that
\begin{align*}
&|\mathbb{E}({\Greekmath 0127}_i^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,jk}{\Greekmath 0117}_{i+k_n}^{n,lm}|\mathcal{F}
_{i-1}^n)-\mathbb{E}\Big({\Greekmath 0127}_i^{n,gh}\Big[\frac{1}{k_n}
(C_{i+k_n}^{n,jl}C_{i+k_n}^{n,km}+C_{i+k_n}^{n,jm}C_{i+k_n}^{n,kl})-\frac{
k_n\Delta_n}{3}\overline{C}_{i+k_n}^{n,jk,lm}\Big]|\mathcal{F}_{i-1}^n\Big)| \\
&\leq K\Delta_n^{3/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,2k_n}^n).
\end{align*}
Next, by combining the successive conditioning together with existing
bounds, we have
\begin{eqnarray*}
|\mathbb{E}({\Greekmath 0127}_i^{n,gh}\overline{C}_{i+k_n}^{n,jk,lm})| & \leq &
K\Delta_n^{1/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,k_n}^n) \\
|\mathbb{E}({\Greekmath 0127}_i^{n,gh}C_{i+k_n}^{n,jl}C_{i+k_n}^{n,km})| & \leq &
K\Delta_n^{1/2},
\end{eqnarray*}
which together imply
\begin{equation}
|\mathbb{E}({\Greekmath 0127}_i^{n,gh}{\Greekmath 0117}_{i+k_n}^{n,jk}{\Greekmath 0117}_{i+k_n}^{n,lm}|\mathcal{F}
_{i-1}^n)|\leq K \Delta_n^{3/4}(\Delta_n^{1/4}+{\Greekmath 0111}_{i,2k_n}^n).
\end{equation}
It is easy to see that equations ((ref)), ((ref)) and
((ref)) and the inequality ${\Greekmath 0111}_{i,k_n}^n \leq
{\Greekmath 0111}_{i,2k_n}^n $ together yield equations ((ref)) and ((ref)
).
\subsection{Proof of Lemma (ref)}
Equation ((ref)) can be proved easily using the bounds of $
{\Greekmath 011A} (u,v)_{i}^{n,gh}$ in equation (E.60).
To show equations ((ref)), ((ref)) and ((ref)),
we set
\begin{equation*}
\overline{\overline{A11}}(H,gh,u;G,ab,v)={\Greekmath 0115} (u,v)_{0}^{n}\sum_{i\in
L^{\prime }\left( n,T\right) }(\partial _{gh}H\partial _{ab}G)(C_{i-1}){\Greekmath 0110}
(u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}.
\end{equation*}
Then,
\begin{equation*}
\frac{1}{\Delta _{n}^{1/4}}\Big(\overline{\overline{A11}}(H,gh,u;G,ab,v)-
\overline{A11}(H,gh,u;G,ab,v)\Big)\overset{\mathbb{P}}{\Rightarrow }0.
\end{equation*}
The above result is proved following similar steps as for equation ((ref)) in case $w=1$ by replacing $\Theta (u,v)_{0}^{(C),i,n}$ by $
{\Greekmath 0115} (u,v)_{0}^{n}((\partial _{gh}H\partial _{ab}G)(C_{i-1})-(\partial
_{gh}H\partial _{ab}G)(C_{i-2k_{n}}))$, which has the same bounds as the
former. Next, decompose $\overline{\overline{A11}}$ as follows,
\begin{align*}
\overline{\overline{A11}}(H,gh,u;G,ab,v)& ={\Greekmath 0115} (u,v)_{0}^{n}\Bigg[
\sum_{i\in L^{\prime }\left( n,T\right) }(\partial _{gh}H\partial
_{ab}G)(C_{i-1})V_{i-1}^{n} \\
& +\sum_{i\in L^{\prime }\left( n,T\right) }(\partial _{gh}H\partial
_{ab}G)(C_{i-1})\Big(\mathbb{E}({\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}|
\mathcal{F}_{i-1}^{n})-V_{i-1}^{n}\Big) \\
& +\sum_{i\in L^{\prime }\left( n,T\right) }(\partial _{gh}H\partial
_{ab}G)(C_{i-1})\Big({\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}-\mathbb{E}
({\Greekmath 0110} (u)_{i}^{n,gh}{\Greekmath 0110} (v)_{i}^{n,ab}|\mathcal{F}_{i-1}^{n})\Big)\Bigg].
\end{align*}
We follow the proof of equation ((ref)) for $w=1$, and we
replace $\Theta (u,v)_{0}^{(C),i,n}$ by ${\Greekmath 0115} (u,v)_{0}^{n}(\partial
_{gh}H\partial _{ab}G)(C_{i-1})$, which satisfies only the condition $
|{\Greekmath 0115} (u,v)_{0}^{n}(\partial _{gh}H\partial _{ab}G)(C_{i-1})|\leq
\widetilde{{\Greekmath 0115} }_{u,v}^{n}$. This calculation shows that the last two
terms in the above decomposition vanish at a rate faster
than $\Delta _{n}^{1/4}$. Therefore,
\begin{equation*}
\frac{1}{\Delta _{n}^{1/4}}\Bigg(\overline{\overline{A11}}
(H,gh,u;G,ab,v)-{\Greekmath 0115} (u,v)_{0}^{n}\Big(\sum_{i\in L^{\prime }\left(
n,T\right) }(\partial _{gh}H\partial _{ab}G)(C_{i-1})V_{i-1}^{n}\Big)\Bigg)
\Rightarrow 0.
\end{equation*}
As a consequence, for $(u,v)=(1,2)$ and $(2,1)$,
\begin{equation*}
\frac{1}{\Delta _{n}^{1/4}}\overline{\overline{A11}}(H,gh,u;G,ab,v)
\Rightarrow 0.
\end{equation*}
The results follow from the following observation,
\begin{eqnarray*}
&&\frac{1}{\Delta _{n}^{1/4}}\Bigg({\Greekmath 0115} (u,v)_{0}^{n}\Big(
\sum_{g,h,a,b=1}^{d}\sum_{i\in L^{\prime }\left( n,T\right) }(\partial
_{gh}H\partial _{ab}G)(C_{i-1})V_{i-1}^{n}(u,v)\Big) \\
&&-\frac{3}{{\Greekmath 0112} ^{2}}\int_{0}^{T}(\partial _{gh}H\partial
_{ab}G)(C_{t})(C_{t}^{ga}C_{t}^{hb}+C_{t}^{gb}C_{t}^{ha})dt\Bigg)\Rightarrow
0, \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for }(u,v)=(2,2), \\
&&\frac{1}{\Delta _{n}^{1/4}}\Bigg(\sum_{g,h,a,b=1}^{d}{\Greekmath 0115} (u,v)_{0}^{n}
\Big(\sum_{i\in L^{\prime }\left( n,T\right) }(\partial _{gh}H\partial
_{ab}G)(C_{i-1})V_{i-1}^{n}(u,v)\Big)-[H(C),G(C)]_{T}\Bigg)\Rightarrow 0, \\
&&\relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{for }(u,v)=(1,1).
\end{eqnarray*}
\captionsetup{font=small,justification=justified}
\FloatBarrier
\section{Numerical Implementation}
We now discuss some details for the numerical implementation of our
estimators. Section (ref) explains how the main
quantities of interest can be expressed in terms of $\left[ H(C),G(C)\right]
_{T}$, where $C$ is the spot variance matrix of all $d$ assets. However, in
practice many quantities of interest involve only a much smaller subset of
assets, which greatly reduces the computational burden.
For example, suppose we want to calculate the variance of the IdioVol for a
single stock, where R-FM is the CAPM, and IdioVol-FM has one volatility
factor -- the market volatility. Then, we only need to consider two assets,
the stock and the market, e.g., SPY, so $d_{S}=d_{F}=1$ and $d=2$. Denote
the relevant spot variance-covariance matrix by
\begin{equation*}
C=\left(
\begin{array}{cc}
C_{11} & C_{12} \\
C_{21} & C_{22}
\end{array}
\right) ,
\end{equation*}
where $C_{22}=C_{F}$ is the spot variance of the market, and $C_{11}$ is the
spot variance of the individual stock. The quantity of interest is
\begin{equation*}
\left[ H(C),H(C)\right] _{T}=\left[ C_{Z1},C_{Z1}\right] _{T},
\end{equation*}
where $C_{Z1,t}=C_{11}-C_{12}C_{22}^{-1}C_{21}$. The estimators in equations
((ref)) and ((ref)) involve the first derivatives $
\partial _{ab}H\left( C\right) $ for $a,b=1,...,d$, which are
\begin{equation*}
\partial _{ab}H\left( C\right) \equiv \frac{\partial H\left( C\right) }{
\partial C_{ab}}=\frac{\partial C_{Zj}}{\partial C_{ab}}=\frac{\partial
\left( C_{11}-C_{12}C_{22}^{-1}C_{21}\right) }{\partial C_{ab}}=\left\{
\begin{array}{cc}
C_{12}C_{22}^{-2}C_{21} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if }\left( a,b\right) =\left( 2,2\right) \\
-C_{22}^{-1}C_{21} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if }\left( a,b\right) =\left( 1,2\right) \\
-C_{12}C_{22}^{-1} & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if }\left( a,b\right) =\left( 2,1\right) \\
1 & \relax\ifmmode\expandafter\text@\else\expandafter\mbox\fi{if }\left( a,b\right) =\left( 1,1\right)
\end{array}
\right.
\end{equation*}
If we are interested in the stock's IdioVol ${\Greekmath 010D} _{Z}$, by equation
((ref)) we also need the volatility factor $\Pi _{t}=G\left(
C_{t}\right) =C_{22,t}$, and $\left[ \Pi ,C_{Z1}\right] _{T}^{c}$. The
derivatives are $\partial _{ab}G\left( C\right) \equiv \partial G\left(
C\right) \left/ \partial C_{ab}\right. =1\left\{ a=b=2\right\} $.
\section{Additional Figures}
\begin{figure}[!h]
\caption{Monthly $R^2$ of two Return Factor Models ($\protect\widehat{R}
^2_{Yj}$): the CAPM (the blue dotted line) and the Fama-French three factor
model (the red solid line). Stocks are represented by tickers (see Table
(ref) for full stock names). }
\end{figure}
\begin{figure}[!h]
\caption{Monthly $R^2$ of two Return Factor Models ($\protect\widehat{R}
^2_{Yj}$): the CAPM (the blue dotted line) and the Fama-French three factor
model (the red solid line). Stocks are represented by tickers (see Table
(ref) for full stock names). }
\end{figure}