Using GARCH-GRNN model to forecast financial time series

Authors:
Weimin Li;Jianwei Liu;Jiajin Le
Affiliations:
College of Computer Science and Technology of Donghua University, Shanghai, China;College of Computer Science and Technology of Donghua University, Shanghai, China;College of Computer Science and Technology of Donghua University, Shanghai, China
Venue:
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Year:
2005

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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.