title: 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.
authors.affiliation: Department of Statistics & Actuarial Science, University of Hong Kong. hcwu@hkusua.hku.hk
authors.forename: Edmond H C
authors.initials: EH
authors.lastname: Wu
authors.forename: Philip L H
authors.initials: PL
authors.lastname: Yu
authors.forename: W K
authors.initials: WK
authors.lastname: Li
mesh_terms: D003198:Computer Simulation; D017059:Models, Econometric; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012306:Risk; D012308:Risk Management; D013997:Time Factors