Extremal quantiles and stock price crashes

Panayiotis C. Andreou, Sofia Anyfantaki, Esfandiar Maasoumi, Carlo Sala

Producció científica: Article en revista indexadaArticleAvaluat per experts


We employ extreme value theory to identify stock price crashes, featuring low-probability events that produce large, idiosyncratic negative outliers in the conditional distribution. Traditional methods employ approximations under Gaussian assumptions and central moments. This is inherently imprecise and susceptible to misspecifications, especially for tail events. We instead propose new definitions and measures for crash risk based on conditional extremal quantiles (CEQ) of idiosyncratic stock returns. CEQ provide information on quantile-specific impact of covariates, and shed light on prior empirical puzzles and shortcomings in identifying crashes. Additionally, to capture the magnitude of crashes, we provide an expected shortfall analysis of the losses due to crash. Our findings have important implications for a burgeoning literature in financial economics that relies on traditional approximations.

Idioma originalAnglès
Número d’article2241223
Pàgines (de-a)703-724
Nombre de pàgines22
RevistaEconometric Reviews
Estat de la publicacióPublicada - 11 d’ag. 2023


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