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.
Highlights
La Niña phases for the realized variance of heating oil price movements, which are likely to be affected via demand and volatility jumps channels, based on an extended heterogenous autoregressive (HAR)–RV
In the case of spot RV (Panel A), we observe that El Niño phases add to the predictive performance of the HAR–RV model when we use an intermediate window length and forecast horizons of up to two years
Niña phases appears to be stronger than the predictive value of El Niño phases in terms of both statistical significance of the test results and in terms of forecast accuracy, which is not surprising given that the latter is associated with the cooling of temperatures and, greater demand for heating oil and a stronger impact on volatility via trading in the market
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. We forecast the monthly realized variance (RV) of movements of the spot and future heating oil price using an extended version of the widely-studied heterogeneous autoregressive (HAR)–RV model [24]. Niño and La Niña phases that occurred during the period from June 1986 to April 2021 In this regard, it is important to note that measuring variance in terms of RV, defined as the sum of squared daily returns of prices observed over a given month (see [25]), provides an observable and unconditional metric of variance, which is otherwise a latent process. La Niña phases for the realized variance of heating oil price movements, which are likely to be affected via demand and volatility jumps channels, based on an extended HAR–RV model. We use the El Niño and La Niña phases based on the SOI data for a robustness check
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