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Risk Estimation With Composite Quantile Regression

Author

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  • Christou, Eliana
  • Grabchak, Michael

Abstract

New methods for the estimation of the popular risk measures expected shortfall (ES) and Value-at-Risk (VaR) are introduced. These are based on a novel variant of composite quantile regression (CQR), which allows for the simultaneous estimation of quantiles at several levels at once. An extensive simulation study is performed, along with a data analysis based on two major US market indices and two financial sector stocks. The results suggest that the method has a good finite sample performance. This is the first methodology to use CQR for risk estimation.

Suggested Citation

  • Christou, Eliana & Grabchak, Michael, 2025. "Risk Estimation With Composite Quantile Regression," Econometrics and Statistics, Elsevier, vol. 33(C), pages 166-179.
    • Handle: RePEc:eee:ecosta:v:33:y:2025:i:c:p:166-179
    DOI: 10.1016/j.ecosta.2022.04.004
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