Degui Li, Oliver Linton, Haoxuan Zhang
arXiv 10 Mar 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2403.06246 · PDF · DOI · OpenAlex · Extracted main text
We propose a new estimator of high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure from noisy and asynchronous high-frequency data collected for an ultra-large number of assets. The noise processes are allowed to be temporally correlated, heteroskedastic, asymptotically vanishing and dependent on the efficient prices. We define a kernel-weighted pre-averaging method to jointly tackle the microstructure noise and asynchronicity issues, and we obtain uniformly consistent estimates for latent prices. We impose a continuous-time factor model with time-varying factor loadings on the price processes, and estimate the common factors and loadings via a local principal component analysis. Assuming a uniform sparsity condition on the idiosyncratic volatility structure, we combine the POET and kernel-smoothing techniques to estimate the spot volatility matrices for both the latent prices and idiosyncratic errors. Under some mild restrictions, the estimated spot volatility matrices are shown to be uniformly consistent under various matrix norms. We provide Monte-Carlo simulation and empirical studies to examine the numerical performance of the developed estimation methodology.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kanaya \ Kristensen (2016) Estimation of stochastic volatility models by nonparametric filtering | 1.000 | 11 | 4 | 100% |
| 2 | Wang et al (2021) Nonparametric estimation of large covariance matrices with conditional sparsity | 1.000 | 8 | 4 | 100% |
| 3 | Chen, Mykland \ Zhang (2020) The five trolls under the bridge: Principal component analysis with asynchronous and noisy high frequency data | 1.000 | 8 | 3 | 100% |
| 4 | Fan, Liao \ Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements (with discussion) | 0.971 | 12 | 5 | 92% |
| 5 | Kong (2018) On the systematic and idiosyncratic volatility with large panel high-frequency data | 0.920 | 9 | 4 | 78% |
| 6 | Bu et al (2023) Nonparametric estimation of large spot volatility matrices for high-frequency financial data | 0.881 | 19 | 5 | 68% |
| 7 | Kristensen (2010) Nonparametric filtering of the realized spot volatility: a kernel-based approach | 0.843 | 3 | 3 | 100% |
| 8 | Kalnina \ Linton (2008) Estimating quadratic variation consistently in the presence of endogenous and diurnal measurement error | 0.737 | 3 | 2 | 100% |
| 9 | Zu \ Boswijk (2014) Estimating spot volatility with high-frequency financial data | 0.737 | 3 | 2 | 100% |
| 10 | Bickel \ Levina (2008) Covariance regularization by thresholding | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Spectral analysis of high-dimensional spot volatility matrix with applications | 0.405 | 1 | 1 |