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MODEL-FREE INFERENCE FOR TAIL RISK MEASURES

Published online by Cambridge University Press:  10 November 2014

Ke-Li Xu
Ke-Li Xu*
Affiliation:
Texas A&M University
*
*Address correspondence to Ke-Li Xu, Department of Economics, Texas A&M University, 3063 Allen Building, 4228 TAMU, College Station, Texas 77843-4228, USA; e-mail: keli.xu@tamu.edu.
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Abstract

Understanding uncertainty in estimating risk measures is important in modern financial risk management. In this paper we consider a nonparametric framework that incorporates auxiliary information available in covariates and propose a family of inferential methods for the value at risk, expected shortfall, and related risk measures. A two-step generalized empirical likelihood test statistic is constructed and is shown to be asymptotically pivotal without requiring variance estimation. We also show its validity when applied to a semiparametric index model. Asymptotic theories are established allowing for serially dependent data. Simulations and an empirical application to Canadian stock return index illustrate the finite sample behavior of the methodologies proposed.

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ARTICLES
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Econometric Theory , Volume 32 , Issue 1 , February 2016 , pp. 122 - 153
Copyright
Copyright © Cambridge University Press 2014 

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