Value at Risk Estimation Using Independent Component Analysis-generalized Autoregressive Conditional Heteroscedasticity (ica-garch) Models

Abstract

We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.

DOI: 10.1142/S0129065706000779

Cite this paper

@article{Wu2006ValueAR, title={Value at Risk Estimation Using Independent Component Analysis-generalized Autoregressive Conditional Heteroscedasticity (ica-garch) Models}, author={Edmond H. C. Wu and Philip L. H. Yu and Wai Keung Li}, journal={International journal of neural systems}, year={2006}, volume={16 5}, pages={371-82} }