Donggyu Kim, Xinyu Song, Yazhen Wang
arXiv 22 Jun 2020 · Statistics — Methodology · publishedJournal of Multivariate Analysis (2022) · 4 citations (OpenAlex)
arXiv:2006.12039 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces unified models for high-dimensional factor-based Ito process, which can accommodate both continuous-time Ito diffusion and discrete-time stochastic volatility (SV) models by embedding the discrete SV model in the continuous instantaneous factor volatility process. We call it the SV-Ito model. Based on the series of daily integrated factor volatility matrix estimators, we propose quasi-maximum likelihood and least squares estimation methods. Their asymptotic properties are established. We apply the proposed method to predict future vast volatility matrix whose asymptotic behaviors are studied. A simulation study is conducted to check the finite sample performance of the proposed estimation and prediction method. An empirical analysis is carried out to demonstrate the advantage of the SV-Ito model in volatility prediction and portfolio allocation problems.
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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 | Kim, D. and Fan, J (2019) Factor garch-itô models for high-frequency data with application to large volatility matrix prediction self | 1.000 | 13 | 4 | 100% |
| 2 | Fan, J., Liao, Y., and Mincheva, M (2013) Large covariance estimation by thresholding principal orthogonal complements | 1.000 | 9 | 4 | 100% |
| 3 | Christensen, K., Kinnebrock, S., and Podolskij, M (2010) Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data | 0.928 | 4 | 3 | 100% |
| 4 | Fan, J. and Kim, D (2018) Robust high-dimensional volatility matrix estimation for high-frequency factor model self | 0.928 | 4 | 3 | 100% |
| 5 | Fan, J., Furger, A., and Xiu, D (2016) Incorporating global industrial classification standard into portfolio allocation: A simple factor-based large covariance matrix… | 0.874 | 6 | 2 | 100% |
| 6 | Aẗ-Sahalia, Y. and Xiu, D (2017) Using principal component analysis to estimate a high dimensional factor model with high-frequency data | 0.843 | 3 | 3 | 100% |
| 7 | Kim, D. and Wang, Y (2016) Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financia… self | 0.737 | 3 | 2 | 100% |
| 8 | Aẗ-Sahalia, Y., Fan, J., and Xiu, D (2010) High-frequency covariance estimates with noisy and asynchronous financial data | 0.644 | 2 | 2 | 100% |
| 9 | Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., and Shephard, N (2011) Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and n… | 0.644 | 2 | 2 | 100% |
| 10 | Bibinger, M., Hautsch, N., Malec, P., Rei, M., et al (2014) Estimating the quadratic covariation matrix from noisy observations: Local method of moments and efficiency | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.