Forecasting volatility of SET with artificial neural network- GARCH models

  • Wichit Khangphukhieo Statistics Research Unit, Department of Mathemetics, Faculty of Science, Mahasarakham University
  • Preut Thanarat Department of New Media, Faculty of Informatics, Mahasarakham University
  • Piyapatr Busababadhin Statistics Research Unit, Department of Mathemetics, Faculty of Science, Mahasarakham University
Keywords: volatility forecast, leverage effect, heteroskedasticity, Levenberg-Marquardt backpropagation

Abstract

This research aimed to compare the effectiveness of two forecasting models, ANN-GARCH and ANN- EGARCH. The two hybrid models were formed by a combination of Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) and Artificial Neural Network (ANN) models, used to forecast the volatility of the Stock Exchange of Thailand index (SET). The results showed that both of the ANN-GARCH and ANN-EGARCH forecasts were highly accurate, measured by root mean square error (RMSE) and mean absolute percentage error (MAPE) values. With key variables included, the models accurately described volatility forecasts.

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Published
2019-01-21
Section
Research Articles