Extreme Value Estimation for Heterogeneous Data

John H. J. Einmahl and Yi He

Journal of Business & Economic Statistics, 2022, vol. 41, issue 1, 255-269

Abstract: We develop a universal econometric formulation of empirical power laws possibly driven by parameter heterogeneity. Our approach extends classical extreme value theory to specifying the tail behavior of the empirical distribution of a general dataset with possibly heterogeneous marginal distributions. We discuss several model examples that satisfy our conditions and demonstrate in simulations how heterogeneity may generate empirical power laws. We observe a cross-sectional power law for the U.S. stock losses and show that this tail behavior is largely driven by the heterogeneous volatilities of the individual assets.

Date: 2022
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DOI: 10.1080/07350015.2021.2008408

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