Risk Estimation With Composite Quantile Regression

Eliana Christou and Michael Grabchak

Econometrics and Statistics, 2025, vol. 33, issue C, 166-179

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.

Keywords: Composite Quantile Regression; Expected Shortfall; Single-Index; Value-at-Risk (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:33:y:2025:i:c:p:166-179

DOI: 10.1016/j.ecosta.2022.04.004

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