Forecasting Oil Price over 150 Years: The Role of Tail Risks

Afees Salisu, Rangan Gupta and Qiang Ji

No 202120, Working Papers from University of Pretoria, Department of Economics

Abstract: In this study, we examine the predictive value of tail risks for oil returns using the longest possible data available for the modern oil industry, i.e., 1859-2020. The Conditional Autoregressive Value at Risk (CAViaR) of Engle & Manganelli (2004) is employed to generate the tail risks for both 1% and 5% VaRs across four variants (Adaptive, Symmetric absolute value, Asymmetric slope and Indirect GARCH) of the CAViaR with the best variant obtained using the Dynamic Quantile test (DQ) test and %Hits. Overall, our proposed predictive model for oil returns that jointly accommodates tail risks associated with the oil market and US financial market improves the out-of-sample forecast accuracy of oil returns in contrast with a benchmark (random walk) model as well as a one-predictor model with own tail risk only. Our results have important implications for academicians, investors and policymakers.

Keywords: Oil returns; Tail risks; Forecasting; Advanced equity markets (search for similar items in EconPapers)
JEL-codes: C22 C53 G15 Q02 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2021-03
New Economics Papers: this item is included in nep-cwa, nep-ene, nep-for, nep-isf, nep-ore and nep-rmg
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Citations: View citations in EconPapers (10)

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