Adjusting covariance matrix for risk management

Leung Ho Philip YU, F.C. NG, Jessica K.W. TING

Research output: Contribution to journalArticlespeer-review

Abstract

The covariance matrix of asset returns can change drastically and generate huge losses in portfolio value under extreme conditions such as market interventions and financial crises. Estimation of the covariance matrix under a chaotic market is often a call to action in risk management. Nowadays, stress testing has become a standard procedure for many financial institutions to estimate the capital requirement for their portfolio holdings under various stress scenarios. A possible stress scenario is to adjust the covariance matrix to mimic the situation under an underlying stress event. It is reasonable that when some covariances are altered, other covariances should vary as well. Recently, Ng et al. proposed a unified approach to determine a proper correlation matrix which reflects the subjective views of correlations. However, this approach requires matrix vectorization and hence it is not computationally efficient for high dimensional matrices. Besides, it only adjusts correlations, but it is well known that high correlations often go together with high standard deviations during a crisis period. To address these limitations, we propose a Bayesian approach to covariance matrix adjustment by incorporating subjective views of covariances. Our approach is computationally efficient and can be applied to high dimensional matrices. Copyright © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Original languageEnglish
Pages (from-to)1681-1699
JournalQuantitative Finance
Volume20
Issue number10
Early online dateApr 2020
DOIs
Publication statusPublished - 2020

Citation

Yu, P. L. H., Ng, F. C., & Ting, J. K. W. (2020). Adjusting covariance matrix for risk management. Quantitative Finance, 20(10), 1681-1699. doi: 10.1080/14697688.2020.1739737

Keywords

  • Covariance matrix
  • Subjective view
  • IFRS 9
  • Stress testing

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