An artificial neural network-GARCH model for international stock return volatility

In-sample and out-of-sample comparisons reveal that the ANN model captures volatility effects overlooked by GARCH, EGARCH and GJR models and produces out- of-sample volatility forecasts which encompass those from other models.

Abstract

We construct a seminonparametric nonlinear GARCH model, based on the Artificial Neural Network (ANN) literature, and evaluate its ability to forecast stock return volatility in London, New York, Tokyo and Toronto. In-sample and out-of-sample comparisons reveal that our ANN model captures volatility effects overlooked by GARCH, EGARCH and GJR models and produces out-of-sample volatility forecasts which encompass those from other models. We also document important differences between volatility in international markets, such as the substantial persistence of volatility effects in Japan relative to North American and European markets.