arXiv 25 Aug 2022 · Econometrics · publishedJournal of Econometrics (2023) · 4 citations (OpenAlex)
arXiv:2208.12323 · PDF · DOI · OpenAlex · Extracted main text
Several large volatility matrix inference procedures have been developed, based on the latent factor model. They often assumed that there are a few of common factors, which can account for volatility dynamics. However, several studies have demonstrated the presence of local factors. In particular, when analyzing the global stock market, we often observe that nation-specific factors explain their own country's volatility dynamics. To account for this, we propose the Double Principal Orthogonal complEment Thresholding (Double-POET) method, based on multi-level factor models, and also establish its asymptotic properties. Furthermore, we demonstrate the drawback of using the regular principal orthogonal component thresholding (POET) when the local factor structure exists. We also describe the blessing of dimensionality using Double-POET for local covariance matrix estimation. Finally, we investigate the performance of the Double-POET estimator in an out-of-sample portfolio allocation study using international stocks from 20 financial markets.
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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 | Fan, J., Y. Liao, and M. Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements | 1.000 | 11 | 3 | 100% |
| 2 | Ahn, S. C. and A. R. Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.894 | 7 | 5 | 71% |
| 3 | Ait-Sahalia, Y. and D. Xiu (2017) Using principal component analysis to estimate a high dimensional factor model with high-frequency data | 0.843 | 3 | 3 | 100% |
| 4 | Fan, J., A. Furger, and D. Xiu (2016) Incorporating global industrial classification standard into portfolio allocation: A simple factor-based large covariance matrix… | 0.843 | 3 | 3 | 100% |
| 5 | Amini, A. A., A. Chen, P. J. Bickel, and E. Levina (2013) Pseudo-likelihood methods for community detection in large sparse networks | 0.811 | 4 | 2 | 100% |
| 6 | Fan, J., H. Liu, and W. Wang (2018) a): Large covariance estimation through elliptical factor models | 0.811 | 4 | 2 | 100% |
| 7 | Alessi, L., M. Barigozzi, and M. Capasso (2010) Improved penalization for determining the number of factors in approximate factor models | 0.644 | 2 | 2 | 100% |
| 8 | Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models | 0.644 | 2 | 2 | 100% |
| 9 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.644 | 2 | 2 | 100% |
| 10 | Choi, I., D. Kim, Y. J. Kim, and N.-S. Kwark (2018) A multilevel factor model: Identification, asymptotic theory and applications | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 63 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups | 0.888 | 10 | 4 |
| 2 | Large Global Volatility Matrix Analysis Based on Observation Structural Information | 0.883 | 16 | 5 |