 On the estimation of Value-at-Risk and Expected Shortfall at extreme levelsEmese Lazar, Jingqi Pan and Shixuan Wang Journal of Commodity Markets, 2024, vol. 34, issue C Abstract: 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. Keywords: Risk models; Value-at-Risk; Expected Shortfall; Semiparametric model; Oil futures (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jocoma:v:34:y:2024:i:c:s2405851324000102 DOI: 10.1016/j.jcomm.2024.100391 Access Statistics for this article Journal of Commodity Markets is currently edited by Marcel Prokopczuk, Betty Simkins and Sjur Westgaard More articles in Journal of Commodity Markets from Elsevier |
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