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Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

Author

Listed:
  • Ruili Sun

    (Zhongyuan Bank Postdoctoral Programme, Zhongyuan Bank, Zhengzhou 450000, China)

  • Tiefeng Ma

    (School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Shuangzhe Liu

    (Faculty of Science and Technology, University of Canberra, Canberra 2601, Australia)

  • Milind Sathye

    (Faculty of Business, Government and Law, University of Canberra, Canberra 2601, Australia)

Abstract

The literature on portfolio selection and risk measurement has considerably advanced in recent years. The aim of the present paper is to trace the development of the literature and identify areas that require further research. This paper provides a literature review of the characteristics of financial data, commonly used models of portfolio selection, and portfolio risk measurement. In the summary of the characteristics of financial data, we summarize the literature on fat tail and dependence characteristic of financial data. In the portfolio selection model part, we cover three models: mean-variance model, global minimum variance (GMV) model and factor model. In the portfolio risk measurement part, we first classify risk measurement methods into two categories: moment-based risk measurement and moment-based and quantile-based risk measurement. Moment-based risk measurement includes time-varying covariance matrix and shrinkage estimation, while moment-based and quantile-based risk measurement includes semi-variance, VaR and CVaR.

Suggested Citation

  • Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:1:p:48-:d:216804
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