The use of GARCH type models and computational-intelligence-based techniques for forecasting financial time series has been proved extremely successful in recent times. In this article, we apply the finite mixture of ARMA-GARCH model instead of AR or ARMA models to compare with the
standard BP and SVM in forecasting financial time series (daily stock market index returns and exchange rate returns). We do not apply the pure GARCH model as the finite mixture of the ARMA-GARCH model outperforms the pure GARCH model. These models are evaluated on five performance metrics
or criteria. Our experiment shows that the SVM model outperforms both the finite mixture of ARMA-GARCH and BP models in deviation performance criteria. In direction performance criteria, the finite mixture of ARMA-GARCH model performs better. The memory property of these forecasting techniques
is also examined using the behavior of forecasted values vis-a-vis the original values. Only the SVM model shows long memory property in forecasting financial returns.
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artificial neural network;
autoregressive moving average;
generalized autoregressive conditional heteroskedastic;
Document Type: Research Article
Department of Statistics, Rajshahi University, Rajshahi, Bangladesh
Department of Statistics, Rajshahi University, Rajshahi, Bangladesh,Institute of Mathematical Sciences, University of Malaya, Kuala Lumpur, Malaysia
Publication date: March 1, 2011