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Title: Extremal quantiles and stock price crashes
Authors: Andreou, Panayiotis 
Anyfantaki, Sofia 
Maasoumi, Esfandiar 
Sala, Carlo 
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: Extremal quantiles;extreme value theory;quantile regression;stock price crashes
Issue Date: 1-Jan-2023
Source: Econometric Reviews, 2023, vol. 42, iss. 9-10, pp. 703 - 724
Volume: 42
Issue: 9-10
Start page: 703
End page: 724
Journal: Econometric Reviews 
Abstract: 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.
ISSN: 07474938
DOI: 10.1080/07474938.2023.2241223
Rights: © Taylor & Francis Group
Type: Article
Affiliation : Cyprus University of Technology 
Durham University Business School 
Bank of Greece 
Emory University 
Ramon Llull University 
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