Volatility Modelling of Multivariate Financial Time Series by Using ICA-GARCH Models

2005 ◽  
pp. 571-579 ◽  
Author(s):  
Edmond H. C. Wu ◽  
Philip L. H. Yu
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Garch Models ◽  
Financial Time ◽  
Multivariate Financial Time Series

Monitoring the mean of multivariate financial time series

2013 ◽  
Vol 30 (3) ◽  
pp. 328-340 ◽  
Author(s):  
Robert Garthoff ◽  
Vasyl Golosnoy ◽  
Wolfgang Schmid
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Financial Time ◽  
The Mean ◽  
Multivariate Financial Time Series

Financial Data Mining Using Flexible ICA-GARCH Models

2010 ◽  
pp. 255-272
Author(s):  
Philip L.H. Yu ◽  
Edmond H.C. Wu ◽  
W.K. Li
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Risk Management ◽  
Independent Component Analysis ◽  
Value At Risk ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Independent Component ◽  
Garch Models ◽  
Financial Time

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.

scholarly journals Multivariate Financial Time-Series Prediction With Certified Robustness

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109133-109143 ◽  
Author(s):  
Hui Li ◽  
Yunpeng Cui ◽  
Shuo Wang ◽  
Juan Liu ◽  
Jinyuan Qin ◽  
...  
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Financial Time ◽  
Multivariate Financial Time Series
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