Forecasting the Realized Variance of Oil-Price Returns Using Machine-Learning: Is there a Role for U.S. State-Level Uncertainty?

Oguzhan Cepni, Rangan Gupta, Daniel Pienaar () and Christian Pierdzioch
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Daniel Pienaar: Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa

No 202205, Working Papers from University of Pretoria, Department of Economics

Abstract: Predicting the variance of oil-price returns is of paramount importance for policymakers and investors. Recent research has focused on whether disaggregate measures of economicpolicy uncertainty provide better forecasts. Given that the United States (U.S.) is a major player in the international oil market, we extend this line of research by exploring by means of machine-learning techniques whether accounting for U.S. state-level measures of economic-policy uncertainty results in more accurate forecasts. We find improvements in forecast accuracy, especially when we study intermediate and long forecast horizons. This finding is robust to various changes in the model configuration (realized variance vs. realized volatility, sample period, recursive vs. rolling-estimation window, loss function of forecast consumers). Understandably, our findings have important implications for oil traders and policy authorities.

Keywords: Oil Price; Realized variance; Forecasting; Machine Learning; Aggregate and Regional Uncertainties (search for similar items in EconPapers)
JEL-codes: C22 C53 D8 Q02 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2022-01
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Citations: View citations in EconPapers (11)

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Journal Article: Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty? (2022)
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