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A Novel Robust Method for Estimating the Covariance Matrix of Financial Returns with Applications to Risk Management

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  • Leccadito, Arturo

    (Université catholique de Louvain, LIDAM/LFIN, Belgium)

  • Staino, Alessandro
  • Toscano, Pietro

Abstract

In this paper we introduce the dynamic Gerber model (DGC) and compare its performance in the prediction of VaR and ES compared to alternative parametric, nonparametric and semiparametric methods to estimate the variance-covariance matrix of returns. Based on ES backtests, the DGC method produces, overall, accurate ES forecasts. Furthermore, we use the Model Confidence Set (MCS) procedure to identify the superior set of models (SSM). For all the portfolios and VaR/ES confidence levels we consider, the DGC is found to belong to the SSM.

Suggested Citation

  • Leccadito, Arturo & Staino, Alessandro & Toscano, Pietro, 2022. "A Novel Robust Method for Estimating the Covariance Matrix of Financial Returns with Applications to Risk Management," LIDAM Discussion Papers LFIN 2022011, Université catholique de Louvain, Louvain Finance (LFIN).
    • Handle: RePEc:ajf:louvlf:2022011
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    Keywords

    VaR ; ES ; Gerber statistic ; parametric methods ; nonparametric methods ; semiparametric methods;
    All these keywords.

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