Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-GARCH) models.
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.
Wu
Edmond H C
EH
Department of Statistics & Actuarial Science, University of Hong Kong. hcwu@hkusua.hku.hk
Yu
Philip L H
PL
Li
W K
WK
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