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December 2020 Inference for conditional value-at-risk of a predictive regression
Yi He, Yanxi Hou, Liang Peng, Haipeng Shen
Ann. Statist. 48(6): 3442-3464 (December 2020). DOI: 10.1214/19-AOS1937

Abstract

Conditional value-at-risk is a popular risk measure in risk management. We study the inference problem of conditional value-at-risk under a linear predictive regression model. We derive the asymptotic distribution of the least squares estimator for the conditional value-at-risk. Our results relax the model assumptions made in (Oper. Res. 60 (2012) 739–756) and correct their mistake in the asymptotic variance expression. We show that the asymptotic variance depends on the quantile density function of the unobserved error and whether the model has a predictor with infinite variance, which makes it challenging to actually quantify the uncertainty of the conditional risk measure. To make the inference feasible, we then propose a smooth empirical likelihood based method for constructing a confidence interval for the conditional value-at-risk based on either independent errors or GARCH errors. Our approach not only bypasses the challenge of directly estimating the asymptotic variance but also does not need to know whether there exists an infinite variance predictor in the predictive model. Furthermore, we apply the same idea to the quantile regression method, which allows infinite variance predictors and generalizes the parameter estimation in (Econometric Theory 22 (2006) 173–205) to conditional value-at-risk in the Supplementary Material. We demonstrate the finite sample performance of the derived confidence intervals through numerical studies before applying them to real data.

Citation

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Yi He. Yanxi Hou. Liang Peng. Haipeng Shen. "Inference for conditional value-at-risk of a predictive regression." Ann. Statist. 48 (6) 3442 - 3464, December 2020. https://doi.org/10.1214/19-AOS1937

Information

Received: 1 September 2019; Published: December 2020
First available in Project Euclid: 11 December 2020

MathSciNet: MR4185815
Digital Object Identifier: 10.1214/19-AOS1937

Subjects:
Primary: 62G20, 62M10, 62P20

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.48 • No. 6 • December 2020
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