Cross-sectional quantile regression for estimating conditional VaR of returns during periods of high volatility

Xenxo Vidal-Llana and Montserrat Guillén

The North American Journal of Economics and Finance, 2022, vol. 63, issue C

Abstract: Evaluating value at risk (VaR) for a firm’s returns during periods of financial turmoil is a challenging task because of the high volatility in the market. We propose estimating conditional VaR and expected shortfall (ES) for a given firm’s returns using quantile regression with cross-sectional (CSQR) data about other firms operating in the same market. An evaluation using US market data between 2000 and 2020 shows that our approach has certain advantages over a CAViaR model. Identification of low-risk firms and a reduction in computing times are additional advantages of the new method described.

Keywords: Extreme values; Expected shortfall; Asset pricing; Risk management (search for similar items in EconPapers)
JEL-codes: G11 G17 G32 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:63:y:2022:i:c:s106294082200170x

DOI: 10.1016/j.najef.2022.101835

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