Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns

• Published in: Journal of applied statistics Vol. 38; no. 3; pp. 533 - 551
• Main Authors: Hossain, Altaf, Nasser, Mohammed
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.03.2011; Taylor and Francis Journals; Taylor & Francis Ltd
• Series: Journal of Applied Statistics
• Subjects: artificial neural network; autoregressive moving average; back propagation; Comparative analysis; Forecasting; Forecasting techniques; generalized autoregressive conditional heteroskedastic; Neural networks; Rates of return; Stochastic models; Studies; support-vector machine; Time series
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664760903521435

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-à-vis the original values. Only the SVM model shows long memory property in forecasting financial returns.