Forecasting Energy Price Volatilities and Correlations: New Evidence From Fractionally Integrated Multivariate Garch Models

Abstract

Energy price volatilities and correlations have been modeled extensively using short-memory multivariate GARCH models. This paper investigates the potential benefits from using multivariate fractionally integrated GARCH models from a forecasting and a risk management perspective. Several multivariate GARCH models for the spot returns on three major energy markets are compared. Our in-sample results show significant evidence of long-memory decay in energy price returns volatilities, leverage effects and time-varying auto-correlations. The one-step ahead forecasting performance of the models is assessed using several robust matrix loss functions by means of three approaches: the Superior Predictive Ability test, the Model Confidence Set and the Value-at-Risk. The results indicate that the multivariate models incorporating long-memory outperform the short-memory benchmarks in forecasting the conditional co-variance matrix and associated risk magnitudes.