Extreme value estimation for heterogeneous data

John H.J. Einmahl, Yi He

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)255-269
Number of pages15
JournalJournal of Business and Economic Statistics
Issue number1
Publication statusPublished - 2023
Externally publishedYes


  • Extreme value theory
  • Financial returns
  • Heterogeneous data
  • Power law
  • Scales

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