Efficient estimation of high-dimensional dynamic covariance by risk factor mapping: Applications for financial risk management

Mike K.P. SO, Thomas W.C. CHAN, Man Ying Amanda CHU

Research output: Contribution to journalArticles

Abstract

This paper aims to explore a modified method of high-dimensional dynamic variance–covariance matrix estimation via risk factor mapping, which can yield a dependence estimation of asset returns within a large portfolio with high computational efficiency. The essence of our methodology is to express the time-varying dependence of high-dimensional return variables using the co-movement concept of returns with respect to risk factors. A novelty of the proposed methodology is to allow mapping matrices, which govern the co-movement of returns, to be time-varying. We also consider the flexible modeling of risk factors by a copula multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model. Through the proposed risk factor mapping model, the number of parameters and the time complexity are functions of a small number of risk factors instead of the number of stocks in the portfolio, making our proposed methodology highly scalable. We adopt Bayesian methods to estimate unknown parameters and various risk measures in the proposed model. The proposed risk mapping method and financial applications are demonstrated by an empirical study of the Hong Kong stock market. The assessment of the effectiveness of the mapping via risk measure estimation is also discussed. Copyright © 2020 Elsevier B.V. All rights reserved.
Original languageEnglish
JournalJournal of Econometrics
Early online dateJun 2020
DOIs
Publication statusE-pub ahead of print - Jun 2020

Citation

So, M. K. P., Chan, T. W. C., & Chu, A. M. Y. (2020). Efficient estimation of high-dimensional dynamic covariance by risk factor mapping: Applications for financial risk management. Journal of Econometrics. Advance online publication. doi: 10.1016/j.jeconom.2020.04.040

Keywords

  • Dynamic covariance modeling
  • Dynamic mapping
  • Multivariate GARCH
  • Risk contribution
  • Tail risk

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