Forecast combining with neural networks

Abstract

This paper investigates the use of Artificial Neural Networks (ANNs) to combine time series forecasts of stock market volatility from the USA, Canada, Japan and the UK. We demonstrate that combining with nonlinear ANNs generally produces forecasts which, on the basis of out-of-sample forecast encompassing tests and mean squared error comparisons, routinely dominate forecasts from traditional linear combining procedures. Superiority of the ANN arises because of its flexibility to account for potentially complex nonlinear relationships not easily captured by traditional linear models.