On the estimation of Value-at-Risk and Expected Shortfall at extreme levels

Lazar, E. ORCID: https://orcid.org/0000-0002-8761-0754, Pan, J. and Wang, S. ORCID: https://orcid.org/0000-0003-2113-5521 (2024) On the estimation of Value-at-Risk and Expected Shortfall at extreme levels. Journal of Commodity Markets, 34. 100391. ISSN 2405-8513 doi: 10.1016/j.jcomm.2024.100391

Abstract/Summary

The estimation of risk at extreme levels (such as 0.1%) can be crucial to capture the losses during market downturns, such as the global financial crisis and the COVID-19 market crash. For many existing models, it is challenging to estimate risk at extreme levels. In order to improve such estimation, we develop a framework to estimate Value-at-Risk and Expected Shortfall at an extreme level by extending the one-factor GAS model and the hybrid GAS/GARCH model to estimate Value-at-Risk and Expected Shortfall for two levels simultaneously, namely for an extreme level and for a more common level (such as 10%). Our simulation results indicate that the proposed models outperform the GAS model benchmarks in terms of in-sample and out-of-sample loss values, as well as backtest rejection rates. We apply the proposed models to oil futures (WTI, Brent, gas oil and heating oil) and compare them with a range of parametric, nonparametric, and semiparametric alternatives. The results show that our proposed models are generally superior to the alternatives.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/115249
Identification Number/DOI	10.1016/j.jcomm.2024.100391
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting Arts, Humanities and Social Science > School of Politics, Economics and International Relations > Economics
Publisher	Elsevier
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