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 applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

Highlights

  • Energy risk has always been one of the major risk factors for most firms involved in key industrial sectors in both developed and developing countries

  • In the class of the support vector regression (SVR) models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases

  • Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the generalized autoregressive conditional heteroscedasticity (GARCH) models when the squared daily return is used as the proxy of volatility in an evaluation

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Summary

Introduction

Energy risk has always been one of the major risk factors for most firms involved in key industrial sectors in both developed and developing countries. Risk management of energy commodities is a crucial issue for majority industrial firms, as it can seriously affect its competitiveness, viability and future profitability. Global economic developments, emerging technological advances and economic, geopolitical and environmental events have caused a significant increase in volatility of energy commodities prices in the last 20 years (cf [1]). For these reasons, the ability to predict volatility of energy commodities has been gaining more and more importance. There are many methods for volatility forecasting but the most popular in the literature are the generalized autoregressive conditional heteroscedasticity (GARCH)

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