A hybrid econometrics and machine learning based modeling of realized volatility of natural gas

Determining which variables afect price realized volatility has always been challenging. This paper proposes to explain how fnancial assets infuence realized volatility by developing an optimal day-to-day forecast. The methodological proposal is based on using the best econometric and machine learning models to forecast realized volatility. In particular, the best forecasting from heterogeneous autoregressive and long short-term memory models are used to determine the infuence of the Standard and Poor's 500 index, euro-US dollar exchange rate, price of gold, and price of Brent crude oil on the realized volatility of natural gas. These fnancial assets infuenced the realized volatility of natural gas in 87.4% of the days analyzed; the euro-US dollar exchange rate was the primary fnancial asset and explained 40.1% of the infuence. The results of the proposed daily analysis difered from those of the methodology used to study the entire period. The traditional model, which studies the entire period, cannot determine temporal efects, whereas the proposed methodology can. The proposed methodology allows us to distinguish the efects for each day, week, or month rather than averages for entire periods, with the fexibility to analyze diferent frequencies and periods. This methodological capability is key to analyzing infuences and making decisions about realized volatility.