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Financial Data Mining Using Flexible ICA-GARCH Models

Financial Data Mining Using Flexible ICA-GARCH Models

Philip L.H. Yu, Edmond H.C. Wu, W.K. Li
Copyright: © 2010 |Pages: 18
ISBN13: 9781605669083|ISBN10: 1605669083|ISBN13 Softcover: 9781616924461|EISBN13: 9781605669090
DOI: 10.4018/978-1-60566-908-3.ch011
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MLA

Yu, Philip L.H., et al. "Financial Data Mining Using Flexible ICA-GARCH Models." Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches, edited by A B M Shawkat Ali and Yang Xiang, IGI Global, 2010, pp. 255-272. https://doi.org/10.4018/978-1-60566-908-3.ch011

APA

Yu, P. L., Wu, E. H., & Li, W. (2010). Financial Data Mining Using Flexible ICA-GARCH Models. In A. Ali & Y. Xiang (Eds.), Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches (pp. 255-272). IGI Global. https://doi.org/10.4018/978-1-60566-908-3.ch011

Chicago

Yu, Philip L.H., Edmond H.C. Wu, and W.K. Li. "Financial Data Mining Using Flexible ICA-GARCH Models." In Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches, edited by A B M Shawkat Ali and Yang Xiang, 255-272. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-908-3.ch011

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Abstract

As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset returns which is a useful tool in portfolio selection and risk management. In the finance literature, the generalized autoregressive conditional heteroscedasticity (GARCH) model and its variants such as EGARCH and GJR-GARCH models have become popular standard tools to model the volatility processes of financial time series. Although univariate GARCH models are successful in modeling volatilities of financial time series, the problem of modeling multivariate time series has always been challenging. Recently, Wu, Yu, & Li (2006) suggested using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series components and then separately modeled the independent components by univariate GARCH models. In this chapter, we extend this class of ICA-GARCH models to allow more flexible univariate GARCH-type models. We also apply the proposed models to compute the value-at-risk (VaR) for risk management applications. Backtesting and out-of-sample tests suggest that the ICA-GARCH models have a clear cut advantage over some other approaches in value-at-risk estimation.

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