Year: 2024
Author: Qing Liu, Weimin Liu, Liang Peng, Gengsheng Qin
Communications in Mathematical Research , Vol. 40 (2024), Iss. 1 : pp. 102–124
Abstract
Value-at-Risk (VaR) and expected shortfall (ES) are two key risk measures in financial risk management. Comparing these two measures has been a hot debate, and most discussions focus on risk measure properties. This paper uses independent data and autoregressive models with normal or $t$-distribution to examine the effect of the heavy tail and dependence on comparing the nonparametric inference uncertainty of these two risk measures. Theoretical and numerical analyses suggest that VaR at 99% level is better than ES at 97.5% level for distributions with heavier tails.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cmr.2022-0071
Communications in Mathematical Research , Vol. 40 (2024), Iss. 1 : pp. 102–124
Published online: 2024-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 23
Keywords: $α$-mixing asymptotic variance expected shortfall Value-at-Risk.