Abstract
Recent researches in forecasting with generalized regression neural network (GRNN) suggest that GRNN can be a promising alternative to the linear and nonlinear time series models. It has shown great abilities in modeling and forecasting nonlinear time series. Generalized autoregressive conditional heteroscedastic (GARCH) model is a popular time series model in forecasting volatility of financial returns. In this paper, a model combined the GARCH and GRNN is proposed to make use of the advantages of both models in linear and nonlinear modeling. In the GARCH-GRNN model, GARCH modeling aids in improving the combined model’s forecasting performance by capturing statistical and volatility information from the time series. The relative tests testify that the combined model can be an effective way to improve forecasting performance achieved by either of the models used separately.
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References
Nelson, D.: Conditional Heteroscedasticity in asset returns: A new approach. Econometrica 59, 347–370 (1991)
Nelson, D.B.: ARCH Models as Diffusion Approximations. Journal of Econometrics 45, 7–38 (1990)
Andersen, T.G., Bollerslev, T.: Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review 39, 885–905 (1998)
Bollerslev, T., Chou, R.Y., Kroner, K.F.: ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence. Journal of Econometrics 52, 5–59 (1992)
Gourieroux, C.: ARCH Models and Financial Applications. Springer, Heidelberg (1997)
Cao, L.J., Tay Francis, E.H.: Financial forecasting using support vector machines. Neural Computing & applications 10, 184–192 (2001)
Qai, M., Zhang, G.P.: An investigation of model selection criteria for neural network time series forecasting. European Journal of Operational Research 132, 102–188 (2001)
Zhang, P.-Y., Lu, T.-S., Song, L.-B.: RBF networks-based inverse kinematics of 6R manipulator. Int J Adv Manuf Technol 26, 144–147 (2005)
Leung, M. T., Chen, A.S., Daouk, H.: Forecasting exchange rates using general regression neural networks. Computers & Operations Research 27, 1093–1110 (2000)
Tang, Z., Almeida, C., Fishwick, P.A.: Time series forecasting using neural networks vs Box–Jenkins methodology. Simulation 57, 303–310 (1991)
Palit, A. K., Popovic, D.: Nonlinear combination of forecasts using artificial neural network, fuzzy logic and neurofuzzy approaches. In: Proceedings of the 9th IEEE International Conference on Fuzzy Systems, Melbourne, Australia (2001)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307–327 (1986)
Engle, R.F.: Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50, 987–1008 (1982)
Specht, D.F.: A general Regression Neural Network. IEEE Transactions on Neural Networks 2, 568–576 (1991)
Pai, P.-F., Lin, C.-S.: Using support vector machines to forecast the production values of the machinery industry in Taiwan. Int J Adv Manuf Technol (2005)
Peter Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2004)
Wasserman, P.D.: Advanced Methods in Neural Computing, pp. 155–161. Van Nostrand Reinhold, New York (1993)
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Li, W., Liu, J., Le, J. (2005). Using GARCH-GRNN Model to Forecast Financial Time Series. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_59
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DOI: https://doi.org/10.1007/11569596_59
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