Forecasting crude oil volatility with uncertainty indicators: New evidence

Xiafei Li, Chao Liang, Zhonglu Chen and Muhammad Umar

Energy Economics, 2022, vol. 108, issue C

Abstract: This paper uses multiple uncertainty indicators to forecast monthly WTI crude oil volatility and compare the predictive performance of combination forecast methods, dimension reduction techniques and two least absolute shrinkage and selection operator augmented MIDAS (MIDAS-LASSO and MS-MIDAS-LASSO) models. Some noteworthy findings are observed by using the MIDAS-RV extensions. First, among all uncertainty indicators, the U.S. petroleum market equity market volatility tracker index (PMEMV) statistically has the best short-term predictive power for the volatility of crude oil market, especially during low volatility, non-crisis and economic expansion periods. However, the geopolitical risk index (GPR) performs better in predicting long-term crude oil volatility than other uncertainty indicators, and it also performs better than other uncertainty indicators in forecasting short-term high volatility of crude oil market. In addition, the financial stress index (FSI) has better predictive ability than other uncertainty indicators during periods of crisis and economic recession. Finally, the newly constructed MS-MIDAS-LASSO and MIDAS-LASSO models always have much higher forecasting accuracy than combination forecast methods, dimension reduction techniques as well as the best MIDAS-RV-X models, with MS-MIDAS-LASSO model having greater forecasting accuracy than the MIDAS-LASSO model in most cases. This empirical finding is confirmed by a variety of robustness checks.

Keywords: Oil volatility forecast; Uncertainty indicators; LASSO; Regime switching (search for similar items in EconPapers)
JEL-codes: C32 C53 G17 Q47 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:108:y:2022:i:c:s0140988322001141

DOI: 10.1016/j.eneco.2022.105936

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