Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches

This paper aims to use both the standard and iterated combination approaches to accurately predict the oil futures market volatility. We further make a comprehensive comparison of the out-of-sample forecasting performance between the two paired combination approaches. According to both the Diebold-Mariano test and model confidence set test, the iterated combination approach generates significantly more accurate volatility forecasts than the standard counterpart. The Direction-of-Change test suggests that the iterated combination approach also has substantially higher directional accuracy. We document that these results are robust to various settings. Furthermore, a mean-variance investor can obtain sizeable economic gains when she uses the iterated combination forecasts instead of the standard ones to allocate her portfolio.