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

Leccadito, Arturo;Staino, Alessandro;Toscano, Pietro
(2022) , 38 pages

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Authors
  • Leccadito, ArturoUCLouvain
    Author
  • Staino, Alessandro
    Author
  • Toscano, Pietro
    Author
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.
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Leccadito, A., Staino, A., & Toscano, P. (2022). A Novel Robust Method for Estimating the Covariance Matrix of Financial Returns with Applications to Risk Management (LIDAM Discussion Paper LFIN 2022/11). https://hdl.handle.net/2078.5/100197