Tail Index Regression

28 Pages Posted: 11 Feb 2009

See all articles by Hansheng Wang

Hansheng Wang

Peking University - Guanghua School of Management

Chih-Ling Tsai

University of California, Davis - Graduate School of Management

Date Written: February 10, 2009

Abstract

In extreme value statistics, the tail index is an important measure to gauge the heavy-tailed behavior of a distribution. Under Pareto-type distributions, we employ the logarithmic function to link the tail index to the linear predictor induced by covariates, which constitutes the tail index regression model. We then propose an approximate log-likelihood function to obtain regression parameter estimators, and subsequently show the asymptotic normality of those estimators. Numerical studies are presented to illustrate theoretical findings.

Keywords: Generalized Extreme Distribution; Generalized Pareto Distribution; Hill Estimator; Pareto-type Distribution; Tail Index Regression

JEL Classification: C10, C13

Suggested Citation

Wang, Hansheng and Tsai, Chih-Ling, Tail Index Regression (February 10, 2009). UC Davis Graduate School of Management Research Paper No. 08-09. Available at SSRN: https://ssrn.com/abstract=1340758 or http://dx.doi.org/10.2139/ssrn.1340758

Hansheng Wang (Contact Author)

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

HOME PAGE: http://hansheng.gsm.pku.edu.cn

Chih-Ling Tsai

University of California, Davis - Graduate School of Management ( email )

One Shields Avenue
Davis, CA 95616
United States

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