El Niño, La Niña, and the Forecastability of the Realized Variance of Heating Oil Price Movements

Abstract

We use the heterogenous autoregressive (HAR) model to compute out-of-sample forecasts of the monthly realized variance (RV) of movements of the spot and futures price of heating oil. We extend the HAR–RV model to include the role of El Niño and La Niña episodes, as captured by the Equatorial Southern Oscillation Index (EQSOI). Using data from June 1986 to April 2021, we show evidence for several model configurations that both El Niño and La Niña phases contain information useful for forecasting subsequent to the realized variance of price movements beyond the predictive value already captured by the HAR–RV model. The predictive value of La Niña phases, however, seems to be somewhat stronger than the predictive value of El Niño phases. Our results have important implications for investors, as well as from the perspective of sustainable decisions involving the environment.

El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach

“…2 At this stage, it is important to describe the underlying economic channel via which we conjecture the link between the ENSO and the realized variance of the returns of the agricultural commodities prices. Recent studies (Balcilar et al, 2021;Bouri et al, 2021;Demirer et al 2022;Qin et al, 2020) have indicated that the ENSO can successfully serve as an empirical proxy for the theoretical concept of rare disaster risks, as coined by Rietz (1988), Barro, (2006Barro, ( , 2009, and Gabaix (2012). The rise of rare disaster risks associated with the ENSO is likely to make the path of future aggregate demand and aggregate production less predictable.…”

Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment

“…At this stage, it is important to indicate that two somewhat related papers are the works of [36,37], which forecasted monthly RVs of heating oil and crude oil prices, respectively, based on the information content of the El Niño Southern Oscillation (ENSO) phases, using a HAR-RV framework. Note that, ref [37] extended the work of [38], which provided in-sample evidence of the role of the ENSO in causing oil returns and volatility based on a nonparametric k-th order quantile causality test.…”