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oai:recercat.cat:2445/1937072025-12-05T05:50:13Zcom_2072_1057col_2072_478808col_2072_478917

Cross-sectional quantile regression for estimating conditional VaR of returns during periods of high volatility Vidal-Llana, Xenxo Guillén, Montserrat Avaluació del risc Valor (Economia) Anàlisi de regressió Risk assessment Value (Economics) Regression analysis 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. 2023-02-16T15:10:29Z 2023-02-16T15:10:29Z 2022-11-17 2023-02-16T15:10:29Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 1062-9408 https://hdl.handle.net/2445/193707 729805 eng Reproducció del document publicat a: https://doi.org/10.1016/j.najef.2022.101835 North American Journal of Economics and Finance, 2022, vol. 63, p. 101835 https://doi.org/10.1016/j.najef.2022.101835 cc-by (c) Vidal-Llana et al., 2022 https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess 9 p. application/pdf Elsevier Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)