2020
DOI: 10.3390/en14010006
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Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

Abstract: We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the… Show more

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Cited by 23 publications
(18 citation statements)
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“…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. Accordingly, we differ from the existing literature (see for example, [26][27][28][29][30][31][32]) on modeling and forecasting heating oil volatility based on various types of univariate GARCH models, under which the conditional variance is a deterministic function of model parameters and historical data, and, hence, is not model-free as in the case of RV. Moreover, as discussed in [33,34], the benchmark HAR-RV model captures the generally observed long-memory and multi-scaling properties of volatility, despite having a simplistic structure.…”
Section: Introductionmentioning
confidence: 83%
See 1 more Smart Citation
“…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. Accordingly, we differ from the existing literature (see for example, [26][27][28][29][30][31][32]) on modeling and forecasting heating oil volatility based on various types of univariate GARCH models, under which the conditional variance is a deterministic function of model parameters and historical data, and, hence, is not model-free as in the case of RV. Moreover, as discussed in [33,34], the benchmark HAR-RV model captures the generally observed long-memory and multi-scaling properties of volatility, despite having a simplistic structure.…”
Section: Introductionmentioning
confidence: 83%
“…In a similar vein, in terms of methodological innovation, ref. [32] compared support vector regression (SVR) with the GARCH models, and showed the superiority of the former approach in forecasting the volatility of heating oil futures based on daily data from 2015 to 2019. However, this study also indicated that, within the GARCH models, asymmetric versions tend to perform better-an observation drawn earlier by [26].…”
Section: Introductionmentioning
confidence: 99%
“…where C is some prespecified positive value (cf. [55]). The first term of Φ(ω, ξ) penalizes large coefficients ω i in order to maintain the flatness of the function f (x), whereas the second one penalizes training errors by using the ε-insensitive loss function [56].…”
Section: Support Vector Regressionmentioning
confidence: 99%
“…Energy commodity risk management is a key issue for most industrial companies, as it can seriously affect their competitiveness and future profitability. Global economic developments, emerging technological advances, and economic, geopolitical, and environmental events have caused a significant increase in the volatility of energy commodity prices over the past 20 years [4]. One such event is the COVID-19 pandemic.…”
Section: Introductionmentioning
confidence: 99%