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A Monte Carlo synthetic sample based performance evaluation method for covariance matrix estimators

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  • Jin Yuan
  • Xianghui Yuan

Abstract

The evaluation of covariance matrix estimators is very important for portfolio analysis and risk management. The Monte Carlo synthetic sample based performance evaluation method proposed by this article can avoid the main shortcomings of statistical and economic methods which are widely used in the existing literature. The proposed method does not need the true covariance and does not need to introduce the performance of the out-of-sample portfolios. It is an intuitive, effective and robust measure for both simulation and empirical analysis.

Suggested Citation

  • Jin Yuan & Xianghui Yuan, 2021. "A Monte Carlo synthetic sample based performance evaluation method for covariance matrix estimators," Applied Economics Letters, Taylor & Francis Journals, vol. 28(2), pages 124-128, January.
    • Handle: RePEc:taf:apeclt:v:28:y:2021:i:2:p:124-128
    DOI: 10.1080/13504851.2020.1738322
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