A New Regression-Based Tail Index Estimator
A New Regression-Based Tail Index Estimator
ABSTRACT
A new regression-based approach for the estimation of the tail index of heavy-tailed distributions with several important properties is introduced. First, it provides a bias reduction when compared to available regression-based methods; second, it is resilient to the choice of the tail length used for the estimation of the tail index; third, when the effect of the slowly varying function at infinity of the Pareto distribution vanishes slowly, it continues to perform satisfactorily; and fourth, it performs well under dependence of unknown form. An approach to compute the asymptotic variance under time dependence and conditional heteroskcedasticity is also provided.
WITH
João Nicolau, ISEG-Universidade de Lisboa
About the Author(s)
Paulo Rodrigues
Paulo M. M. Rodrigues obtained his PhD in Econometrics from the School of Economic Studies of the University of Manchester. He is a professor of economics at Nova School of Business and Economics, a senior research economist at the Bank of Portugal, and a fellow of both the Clive Granger Centre for Time Series Analysis of the University of Nottingham and the University of Essex´s Centre for Financial Econometrics.