Forecasting the Conditional Distribution of Realized Volatility of Oil Price Returns: The Role of Skewness over 1859 to 2023

Rangan Gupta, Qiang Ji, Christian Pierdzioch and Vasilios Plakandaras
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Qiang Ji: Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China

No 202318, Working Papers from University of Pretoria, Department of Economics

Abstract: We examine the predictive value of expected skewness of oil returns for the corresponding realized volatility using monthly data for the entire modern history of the oil industry, covering 1859:11 to 2023:04. We utilize a quantile predictive regression model, which is able to accommodate nonlinearity and structural breaks. In-sample results show that the predictive impact of expected skewness on realized volatility can be both positive and negative, with these signs contingent on the quantiles of realized volatility. Moreover, we detected statistically significant forecasting gains that arise at the extreme ends and around the median of the conditional distribution of realized volatility, at 1-, 3-, 6- and, particularly, 12-month-ahead horizons. Our results have important implications for academics, investors and policymakers.

Keywords: Oil Returns; Expected Skewness; Realized Volatility; Quantile Regression; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 Q02 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2023-06
New Economics Papers: this item is included in nep-ene and nep-rmg
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Citations: View citations in EconPapers (3)

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Journal Article: Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023 (2023)
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