Forecasting the Volatility of US Oil and Gas Firms With Machine Learning

Forecasting the realized volatility of oil and gas firms is of interest to investors and practitioners trading on the energy spot and derivative markets. In this paper, we assess whether several machine learning (ML) techniques can offer superior forecasts compared to HAR models for predicting realized volatility at the firm level. Moreover, we investigate whether economically motivated variables and technical indicators contain valuable information for forecasting firm volatility beyond those contained in various volatility factors previously identified in the literature. Our results demonstrate that certain ML techniques provide superior forecasting accuracy compared to the benchmark model. Additionally, we identify variables such as the 1-month treasury bill and the aggregate VIX index as significant drivers of realized firm volatility in the oil and gas industry.