Financial Prediction Using Manifold Wavelet Kernel

Author

Tang, LingBing ; Sheng, Huanye

Author_Institution

Dept. of Comput. & Electron. Eng., Hunan Bus. Coll., Changsha, China

fYear

2009

fDate

6-7 June 2009

Firstpage

63

Lastpage

65

Abstract

This paper constructs an admissible manifold wavelet kernel (MWK) for support vector machine (SVM) to forecast the volatility of financial time series based on generalized autoregressive conditional heteroscedasticity (GARCH) model. The MWK is obtained by incorporating the wavelet technique and manifold theory into SVM. Unlike Gaussian kernel in SVM, the MWK can approximate arbitrary nonlinear functions. The applicability and validity of MWK for volatility forecast are confirmed through experiments on simulated data sets.

Keywords

autoregressive processes; finance; nonlinear functions; support vector machines; time series; wavelet transforms; Gaussian kernel; financial prediction; financial time series; generalized autoregressive conditional heteroscedasticity model; manifold wavelet kernel; nonlinear function; support vector machine; Computational modeling; Computer science; Educational institutions; Kernel; Manifolds; Polynomials; Predictive models; Risk management; Support vector machine classification; Support vector machines; GARCH forecast; MWK;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Mining and Web-based Application, 2009. WMWA '09. Second Pacific-Asia Conference on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3646-0

Type

conf

DOI

10.1109/WMWA.2009.77

Filename

5232468