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Large Global Volatility Matrix Analysis Based on Observation Structural Information

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  • Sung Hoon Choi
  • Donggyu Kim

    (Department of Economics, University of California Riverside)

Abstract

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 (S-POET), incorporates observation structural information for both global and national factor models. We establish the asymptotic properties of the S-POET estimator, and also demonstrate the drawbacks of conventional covariance matrix estimation procedures when using lower-frequency data. Finally, we apply the S-POET estimator to an out-of-sample portfolio allocation study using international stock market data.

Suggested Citation

  • Sung Hoon Choi & Donggyu Kim, 2024. "Large Global Volatility Matrix Analysis Based on Observation Structural Information," Working Papers 202424, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202424
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    References listed on IDEAS

    as
    1. Yucheng Sun & Wen Xu, 2022. "A Factor-Based Estimation of Integrated Covariance Matrix With Noisy High-Frequency Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 770-784, April.
    2. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2006. "Analysis of high dimensional multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 134(2), pages 341-371, October.
    3. Jianqing Fan & Alex Furger & Dacheng Xiu, 2016. "Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 489-503, October.
    4. Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019. "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, vol. 208(1), pages 43-79.
    5. Xu Han, 2021. "Shrinkage Estimation of Factor Models With Global and Group-Specific Factors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 1-17, January.
    6. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
    7. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    Full references (including those not matched with items on IDEAS)

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