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Change-Point Testing for Risk Measures in Time Series

Lin Fan, Junting Duan, Peter W. Glynn, Markus Pelger

arXiv 7 Sep 2018 · Econometrics · 1 citations (OpenAlex)

arXiv:1809.02303 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose novel methods for change-point testing for nonparametric estimators of expected shortfall and related risk measures in weakly dependent time series. We can detect general multiple structural changes in the tails of marginal distributions of time series under general assumptions. Self-normalization allows us to avoid the issues of standard error estimation. The theoretical foundations for our methods are functional central limit theorems, which we develop under weak assumptions. An empirical study of S&P 500 and US Treasury bond returns illustrates the practical use of our methods in detecting and quantifying instability in the tails of financial time series.

Citation extraction

46
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in-text mentions
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distinct cited
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main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1T. Zhang and L. Lavitas (2018) Unsupervised self-normalized change-point testing for time series0.87482100%
2X. Shao and X. Zhang (2010) Testing for change points in time series0.87462100%
3S.X. Chen (2008) Nonparametric estimation of expected shortfall0.84333100%
4S.X. Chen and C.Y. Tang (2005) Nonparametric inference of value-at-risk for dependent financial returns0.84333100%
5S. Asmussen and P.W. Glynn (2007) Stochastic Simulation: Algorithms and Analysis0.73732100%
6I.N. Lobato (2001) Testing that a dependent process is uncorrelated0.73732100%
7X. Shao (2010) A self-normalized approach to confidence interval construction in time series0.73732100%
8M. Csorgo and L. Horvath (1997) Limit Theorems in Change-Point Analysis0.64422100%
9P.W. Glynn and D.L. Iglehart (1990) Simulation output analysis using standardized time series self0.64422100%
10J.E. Methni, L. Gardes, and S. Girard (2014) Non‐parametric estimation of extreme risk measures from conditional heavy‐tailed distributions0.64422100%

Showing the top 10 of 46 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Quantile Time Series Regression Models Revisited0.40511