Modeling and Forecasting the Volatility of Gas Futures Prices

Abstract

We examine the ability of three different GARCH-class models, with four innovation distributions, to capture the volatility properties of natural gas futures contracts traded on the New York Mercantile Exchange. We jointly estimate the long-memory processes for conditional return and variance investigating the long-memory and persistence of long and short maturities contracts. We examine the ability of these models and distributions to forecast the conditional variance. We find that AR(FI)MA-FIAPARCH model is a better fit for short- and long-term contracts. However, there is not a single innovation distribution that provides a better fit for all of the data examined. The out-of- sample forecast of variance also provides mixed results concerning the best innovation distribution. Further, the persistence decreases as the maturity of contracts increases.