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Forecast combining with neural networks

✍ Scribed by R. Glen Donaldson; Mark Kamstra

Publisher: John Wiley and Sons
Year: 1996
Tongue: English
Weight: 987 KB
Volume: 15
Category: Article
ISSN: 0277-6693
DOI: 10.1002/(sici)1099-131x(199601)15:1<49::aid-for604>3.0.co;2-2

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✦ Synopsis

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. KEY WORDS forecast combing; artificial neural network; encompassing test 'The forecast combining literature is much too vast to adequately cite here. For excellent reviews of the forecasting literature and discussions of traditional weight-selection techniques, however, see Clemen (1989), Granger (1989) and Min and Zellner (1993).

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