Covariance Matrix Estimation: A Comparative Analysis

Abstract

For accurate risk assessment and portfolio optimization in finance, the covariance matrix is crucial. This research evaluates various estimation techniques, comparing their accuracy and limitations. Starting with the sample covariance matrix, the study explores both static and dynamic shrinkage approaches and concludes with a factor copula model to offer a different perspective on estimation. Through simulations and empirical analysis of high-dimensional stock market data, the study demonstrates that dynamic models outperform static ones in accuracy, even though they come at a higher computational cost. These findings underscore the need for financial practitioners to select models based on their specific requirements for accuracy, computational efficiency, and application context. Future research should investigate a broader range of assets and additional techniques to further enhance decision-making in covariance matrix estimation.