Extreme Value Estimation for Heterogeneous Data
将经典极值理论扩展到异质性数据，提出一个通用的计量经济学框架来刻画经验幂律分布，并用美国股票损失数据验证了异质性波动率对尾部行为的影响。
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.