Modeling natural gas market volatility using GARCH with different distributions

• Simple linear GARCH-class models beat nonlinear models in forecasting price volatility.
• Nonlinear models allowing for asymmetric effects beat linear models in forecasting basis volatility.
• Forecasting accuracy can be significantly improved by taking the role of fat-tail distribution into account.
• GARCH models cannot capture multifractality in natural gas markets.

In this paper, we model natural gas market volatility using GARCH-class models with long memory and fat-tail distributions. First, we forecast price volatilities of spot and futures prices. Our evidence shows that none of the models can consistently outperform others across different criteria of loss functions. We can obtain greater forecasting accuracy by taking the stylized fact of fat-tail distributions into account. Second, we forecast volatility of basis defined as the price differential between spot and futures. Our evidence shows that nonlinear GARCH-class models with asymmetric effects have the greatest forecasting accuracy. Finally, we investigate the source of forecasting loss of models. Our findings based on a detrending moving average indicate that GARCH models cannot capture multifractality in natural gas markets. This may be the plausible explanation for the source of model forecasting losses.