Prediction on Fund Volatility Based on SVRGM-GARCH Model

Li Yan Geng; Yi Gang Liang

doi:10.4028/www.scientific.net/AMR.403-408.3763

Paper Titles

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Modeling of Doped ZnO Waveguides for Nonlinear Applications
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Modeling and Control of a Complex Interacting Process
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Prediction on Fund Volatility Based on SVRGM-GARCH Model
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Design, Modeling and Simulation of CMOS-MEMS Piezoresistive Cantilever Based Carbon Dioxide Gas Sensor for Capnometry
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Optimized Design for Cockpit Protective Glass Construction against High Power Microwave Using Genetic Algorithm
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Cockpit Protection Analysis for Digital Pilot against Soft Kill from High Power Microwave
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A Design of Thermo-Electrostatic Actuated Microelectromechanical Switch with Low Voltage and Fabrication Efforts
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Prediction on Fund Volatility Based on SVRGM-GARCH Model

Abstract:

GM-GARCH model is a new hybrid volatility model which integrates grey forecasting model (GM (1,1)) into GARCH model. As for the limitation of the parameters estimation algorithm of GM (1,1) model, a SVRGM-GARCH model is established to enhance volatility forecasting performance further. Firstly, support vector machines for regression (SVR) is utilized to estimate the parameters of GM (1,1) model (SVRGM). Then, the SVRGM model is used to modify the random error term sequence of GARCH model. An empirical research is performed on SSE Fund Index and SZSE Fund Index. The result shows that the SVRGM-GARCH model outperforms the GM-GARCH models and GARCH model, which indicates the model proposed in this study is an effective method for volatility forecasting.