Forecasting energy price volatilities and comovements: New evidences from fractionally integrated multivariate GARCH models

Energy price volatilities and correlations have been modelled extensively with short memory multivariate GARCH models. This paper investigates the potential benefits deriving from the use of multivariate fractionally integrated GARCH models from a forecasting and a risk management perspective. Several multivariate GARCH models for the spot returns on three majoir energy markets are compared. Our in-sample results show significant evidence of long memory decay in energy price returns volatilities, of leverage effects and of time-varying autocorrelations. The forecasting performance of the models is assessed by means of three different approaches: the SPA test, the Model Confidence Set and the Value at Risk. The results seem to indicate that the multivariate models incorporating long-memory outperform the short memory benchmarks in forecasting the one day ahead conditional covariance matrix and associated magnitudes, such as VaR forecasting.