A simple but powerful tail index regression

TL;DR

This paper proposes a novel regression-based framework for estimating the conditional tail index of heavy-tailed distributions, enabling easier inference and demonstrating strong finite sample performance through simulations and empirical analysis.

Contribution

It introduces a flexible tail index regression framework using standard econometric methods, notably OLS, for improved estimation and inference in heavy-tailed distribution analysis.

Findings

OLS provides promising results for tail index estimation.

Monte Carlo simulations show good finite sample properties.

Empirical analysis reveals determinants of commodity return tail behavior.

Abstract

This paper introduces a flexible framework for the estimation of the conditional tail index of heavy tailed distributions. In this framework, the tail index is computed from an auxiliary linear regression model that facilitates estimation and inference based on established econometric methods, such as ordinary least squares (OLS), least absolute deviations, or M-estimation. We show theoretically and via simulations that OLS provides interesting results. Our Monte Carlo results highlight the adequate finite sample properties of the OLS tail index estimator computed from the proposed new framework and contrast its behavior to that of tail index estimates obtained by maximum likelihood estimation of exponential regression models, which is one of the approaches currently in use in the literature. An empirical analysis of the impact of determinants of the conditional left- and right-tail indexes of commodities' return distributions highlights the empirical relevance of our proposed approach. The novel framework's flexibility allows for extensions and generalizations in various directions, empowering researchers and practitioners to straightforwardly explore a wide range of research questions.