arXiv 5 Dec 2024 · Econometrics
arXiv:2412.04293 · PDF · DOI · OpenAlex · Extracted main text
Several approaches for predicting large volatility matrices have been developed based on high-dimensional factor-based It\^o processes. These methods often impose restrictions to reduce the model complexity, such as constant eigenvectors or factor loadings over time. However, several studies indicate that eigenvector processes are also time-varying. To address this feature, this paper generalizes the factor structure by representing the integrated volatility matrix process as a cubic (order-3 tensor) form, which is decomposed into low-rank tensor and idiosyncratic tensor components. To predict conditional expected large volatility matrices, we propose the Projected Tensor Principal Orthogonal componEnt Thresholding (PT-POET) procedure and establish its asymptotic properties. The advantages of PT-POET are validated through a simulation study and demonstrated in an application to minimum variance portfolio allocation using high-frequency trading data.
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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 | Shin, M., D. Kim, Y. Wang, and J. Fan (2021) Factor and idiosyncratic VAR-Itô volatility models for heavy-tailed high-frequency financial data | 1.000 | 6 | 5 | 100% |
| 2 | Ait-Sahalia, Y. and D. Xiu (2017) Using principal component analysis to estimate a high dimensional factor model with high-frequency data | 1.000 | 6 | 4 | 100% |
| 3 | Kim, D. and J. Fan (2019) Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction self | 1.000 | 6 | 4 | 100% |
| 4 | Fan, J., Y. Liao, and M. Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements | 1.000 | 5 | 4 | 100% |
| 5 | Jacod, J., Y. Li, P. A. Mykland, M. Podolskij, and M. Vetter (2009) Microstructure noise in the continuous case: the pre-averaging approach | 0.928 | 4 | 3 | 100% |
| 6 | Fan, J., Y. Liao, and W. Wang (2016) b): Projected principal component analysis in factor models | 0.874 | 5 | 2 | 100% |
| 7 | Christensen, K., S. Kinnebrock, and M. Podolskij (2010) Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data | 0.843 | 3 | 3 | 100% |
| 8 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.811 | 4 | 2 | 100% |
| 9 | Aẗ-Sahalia, Y. and D. Xiu (2016) Increased correlation among asset classes: Are volatility or jumps to blame, or both? | 0.737 | 3 | 2 | 100% |
| 10 | Chen, E. Y., D. Xia, C. Cai, and J. Fan (2024) Semi-parametric tensor factor analysis by iteratively projected singular value decomposition | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 57 scored citations.