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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/193707
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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VIDAL-LLANA, Xenxo and GUILLÉN, Montserrat. Cross-sectional quantile regression for estimating conditional VaR of returns during periods of high volatility. North American Journal of Economics and Finance. 2022. Vol. 63, num. 101835. ISSN 1062-9408. [consulted: 15 of August of 2026]. Available at: https://hdl.handle.net/2445/193707