Extremal quantiles and stock price crashes

Andreou, Panayiotis; Anyfantaki, Sofia; Maasoumi, Esfandiar; Sala, Carlo

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.

Citation

Andreou, P., Anyfantaki, S., Maasoumi, E., & Sala, C. (2023). Extremal quantiles and stock price crashes. Econometric Reviews, 42(9-10), 703-724 . https://doi.org/10.1080/07474938.2023.2241223

• Journal Article Type: Article
• Acceptance Date: Jul 25, 2023
• Online Publication Date: Aug 20, 2023
• Publication Date: 2023
• Deposit Date: Aug 22, 2023
• Publicly Available Date: Aug 21, 2024
• Journal: Econometric Reviews
• Print ISSN: 0747-4938
• Electronic ISSN: 1532-4168
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Issue: 9-10
• Pages: 703-724
• DOI: https://doi.org/10.1080/07474938.2023.2241223
• Public URL: https://durham-repository.worktribe.com/output/1722766