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

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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.

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North American Journal of Economics and Finance, 2022, vol. 63, p. 101835

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Articles publicats en revistes  (Econometria, Estadística i Economia Aplicada)

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