Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-garch) models

Research output: Contribution to journalArticles

11 Citations (Scopus)

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. Copyright © 2006 World Scientific Publishing Company.
Original languageEnglish
Pages (from-to)371-382
JournalInternational Journal of Neural Systems
Volume16
Issue number5
DOIs
Publication statusPublished - Oct 2006

Citation

Wu, E. H. C., Yu, P. L. H., & Li, W. K. (2006). Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-garch) models. International Journal of Neural Systems, 16(5), 371-382. doi: 10.1142/S0129065706000779

Fingerprint Dive into the research topics of 'Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-garch) models'. Together they form a unique fingerprint.