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

IF 3.7 4区 经济学 Q1 BUSINESS, FINANCE
Emese Lazar , Jingqi Pan , Shixuan Wang
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引用次数: 0

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.

关于极端水平下风险价值和预期亏损的估算
对极端水平(如 0.1%)的风险进行估算,对于捕捉市场衰退期(如全球金融危机和 COVID-19 市场崩盘)的损失至关重要。对于许多现有模型而言,估算极端水平的风险具有挑战性。为了改进这种估算,我们开发了一个框架,通过扩展单因子 GAS 模型和混合 GAS/GARCH 模型来估算极端水平的风险价值和预期亏空,从而同时估算两个水平的风险价值和预期亏空,即极端水平和更常见的水平(如 10%)。我们的模拟结果表明,所提出的模型在样本内和样本外损失值以及回溯测试拒绝率方面均优于 GAS 模型基准。我们将提出的模型应用于石油期货(WTI、布伦特、天然气油和取暖油),并与一系列参数、非参数和半参数替代模型进行比较。结果表明,我们提出的模型总体上优于其他模型。
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来源期刊
CiteScore
5.70
自引率
2.40%
发文量
53
期刊介绍: The purpose of the journal is also to stimulate international dialog among academics, industry participants, traders, investors, and policymakers with mutual interests in commodity markets. The mandate for the journal is to present ongoing work within commodity economics and finance. Topics can be related to financialization of commodity markets; pricing, hedging, and risk analysis of commodity derivatives; risk premia in commodity markets; real option analysis for commodity project investment and production; portfolio allocation including commodities; forecasting in commodity markets; corporate finance for commodity-exposed corporations; econometric/statistical analysis of commodity markets; organization of commodity markets; regulation of commodity markets; local and global commodity trading; and commodity supply chains. Commodity markets in this context are energy markets (including renewables), metal markets, mineral markets, agricultural markets, livestock and fish markets, markets for weather derivatives, emission markets, shipping markets, water, and related markets. This interdisciplinary and trans-disciplinary journal will cover all commodity markets and is thus relevant for a broad audience. Commodity markets are not only of academic interest but also highly relevant for many practitioners, including asset managers, industrial managers, investment bankers, risk managers, and also policymakers in governments, central banks, and supranational institutions.
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