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Oil tail-risk forecasts: from financial crisis to COVID-19
Risk Management ; 2022.
Article in English | Web of Science | ID: covidwho-2016982
ABSTRACT
The coronavirus outbreak has caused unprecedented volatility in oil prices. This paper extends previous studies on oil Value-at-Risk (VaR) by providing extra insights into Expected Shortfall (ES) forecasting over the last decade, including several oil crises. We introduce a conditional volatility model combined with the Cornish-Fisher expansion for ES forecasting. In comparison to the widely used volatility models and innovation distributions, this approach is superior for predicting the ES of long positions but overestimates VaR for short positions. Overall, the volatility model addressing leverage effects with skewed t innovation produces the most accurate joint VaR and ES forecasting. Moreover, the magnitude of ES relative to VaR varies across models and time, implying that ES should be used in conjunction with VaR to inform timely risk management decisions. The results would be of interest to the regulatory authorities, energy companies, and financial institutions for oil tail-risk forecasting.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: Risk Management Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: Risk Management Year: 2022 Document Type: Article