Tail index estimation in the presence of covariates: Stock returns’ tail risk dynamics

João Nicolau, Paulo Rodrigues and Marian Z. Stoykov

Journal of Econometrics, 2023, vol. 235, issue 2, 2266-2284

Abstract: This paper provides novel theoretical results for the estimation of the conditional tail index of Pareto and Pareto-type distributions in a time series context. We show that both the estimators and relevant test statistics are normally distributed in the limit, when independent and identically distributed or dependent data are considered. Simulation results provide support for the theoretical findings and highlight the good finite sample properties of the approach in a time series context. The proposed methodology is then used to analyse stock returns’ tail risk dynamics. Two empirical applications are provided. The first consists in testing whether the time-varying tail exponents across firms follow Kelly and Jiang’s (2014) assumption of common firm level tail dynamics. The results obtained from our sample seem not to favour this hypothesis. The second application, consists of the evaluation of the impact of two market risk indicators, VIX and Expected Shortfall (ES) and two firm specific covariates, capitalization and market-to-book on stocks tail risk dynamics. Although all variables seem important drivers of firms’ tail risk dynamics, it is found that ES and firms’ capitalization seem to have overall wider impact.

Keywords: Extreme value theory; Pareto-type distributions; Tail index; Covariates information (search for similar items in EconPapers)
JEL-codes: C22 C58 G12 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:2266-2284

DOI: 10.1016/j.jeconom.2023.04.002

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