Vidal-Llana, XenxoGuillén, Montserrat2023-02-162023-02-162022-11-171062-9408https://hdl.handle.net/2445/193707Evaluating 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.9 p.application/pdfengcc-by (c) Vidal-Llana et al., 2022https://creativecommons.org/licenses/by/4.0/Avaluació del riscValor (Economia)Anàlisi de regressióRisk assessmentValue (Economics)Regression analysisinfo:eu-repo/semantics/article7298052023-02-16info:eu-repo/semantics/openAccess