Does Uncertainty Forecast Crude Oil Volatility before and during the COVID-19 Outbreak? Fresh Evidence Using Machine Learning Models

Kais Tissaoui (), Taha Zaghdoudi, Abdelaziz Hakimi, Ousama Ben-Salha and Lamia Ben Amor
Additional contact information
Kais Tissaoui: Management Information Systems Department, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia
Abdelaziz Hakimi: V.P.N.C Lab, Department of Management, Faculty of Law, Economics and Management of Jendouba, University of Jendouba, Jendouba 8189, Tunisia
Lamia Ben Amor: Management Information Systems Department, Applied College, University of Ha’il, Hail City P.O. Box 2440, Saudi Arabia

Energies, 2022, vol. 15, issue 15, 1-20

Abstract: This paper uses two competing machine learning models, namely the Support Vector Regression (SVR) and the eXtreme Gradient Boosting (XGBoost) against the Autoregressive Integrated Moving Average ARIMAX (p,d,q) model to identify their predictive performance of the crude oil volatility index before and during COVID-19. In terms of accuracy, forecasting results reveal that the SVR model dominates the XGBoost and ARIMAX models in predicting the crude oil volatility index before COVID-19. However, the XGBoost model provides more accurate predictions of the crude oil volatility index than the SVR and ARIMAX models during the pandemic. The inverse cumulative distribution of residuals suggests that both ML models produce good results in terms of convergence. Findings also indicate that there is a fast convergence to the optimal solution when using the XGBoost model. When analyzing the feature importance, the Shapley Additive Explanation Method reveals that the SVR performs significantly better than the XGBoost in terms of feature importance. During the pandemic, the predictive power of the CBOE Volatility Index and Economic Policy Uncertainty index for forecasting the crude oil volatility index is improved compared to the pre-COVID-19 period. These findings imply that investor fear-induced uncertainty in the financial market and economic policy uncertainty are the most significant features and hence represent substantial sources of uncertainty in the oil market.

Keywords: crude oil volatility index; uncertainty indexes; complex relationship; Support Vector Machine; eXtreme Gradient Boosting; Shapley Additive Explanation Method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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