Forecasting the Oil Volatility Index Using Factors of Uncertainty

Panagiotis Delis, Stavros Degiannakis and Konstantinos Giannopoulos ()

Asian Journal of Economics and Empirical Research, 2022, vol. 9, issue 1, 13-20

Abstract: The oil volatility index (OVX) has attracted the attention of investors, as oil prices have been subject to high degrees of variation in the last few decades, and investors would therefore benefit from obtaining accurate forecasts of OVX. In this paper, we aim to develop models that can accurately generate OVX forecasts. The contribution of our study to the literature lies in the incorporation of different factors that reflect uncertainty as potential drivers of OVX. For example, implied volatility (IV) indices, such as the VIX and GVZ are examined. Apart from the inclusion of IV indices, we investigate whether other uncertainty indicators play a significant role in generating OVX forecasts. Our results show that the predictive ability of the models is not enhanced by the inclusion of most of the aforementioned factors of uncertainty, with the single exception of the U.S. economic policy uncertainty index, which seems to improve the forecasting ability of a simple model that focuses on the OVX as a target variable.

Keywords: Volatility forecasting; Crude oil; Implied volatility; Uncertainty; Out-of-sample forecasting; Statistical loss functions. (search for similar items in EconPapers)
Date: 2022
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