arXiv 2 May 2023 · Econometrics · publishedEconometric Theory (2024)
arXiv:2305.01464 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we develop a novel large volatility matrix estimation procedure for analyzing global financial markets. Practitioners often use lower-frequency data, such as weekly or monthly returns, to address the issue of different trading hours in the international financial market. However, this approach can lead to inefficiency due to information loss. To mitigate this problem, our proposed method, called Structured Principal Orthogonal complEment Thresholding (Structured-POET), incorporates observation structural information for both global and national factor models. We establish the asymptotic properties of the Structured-POET estimator, and also demonstrate the drawbacks of conventional covariance matrix estimation procedures when using lower-frequency data. Finally, we apply the Structured-POET estimator to an out-of-sample portfolio allocation study using international stock market data.
appendix boundary found by appendix_command · 39% of the source is main text. Read the extracted text to check this.
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., A. Furger, and D. Xiu (2016) Incorporating global industrial classification standard into portfolio allocation: A simple factor-based large covariance matrix… | 0.928 | 4 | 3 | 100% |
| 2 | Choi, S. H. and D. Kim (2023) Large volatility matrix analysis using global and national factor models self | 0.883 | 16 | 5 | 69% |
| 3 | Ahn, S. C. and A. R. Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.843 | 3 | 3 | 100% |
| 4 | Fan, J., Y. Liao, and M. Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.737 | 4 | 3 | 50% |
| 5 | Bekaert, G., R. J. Hodrick, and X. Zhang (2009) International stock return comovements | 0.737 | 3 | 2 | 100% |
| 6 | Fama, E. F. and K. R. French (2012) Size, value, and momentum in international stock returns | 0.737 | 3 | 2 | 100% |
| 7 | Fan, J., H. Liu, and W. Wang (2018) a): Large covariance estimation through elliptical factor models | 0.737 | 3 | 2 | 100% |
| 8 | Ait-Sahalia, Y. and D. Xiu (2017) Using principal component analysis to estimate a high dimensional factor model with high-frequency data | 0.644 | 2 | 2 | 100% |
| 9 | Ando, T. and J. Bai (2017) Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures | 0.644 | 2 | 2 | 100% |
| 10 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.