Value at risk estimation using independent component analysis-generalized autoregressive conditional...

Area of Science:

• Econometrics
• Computational Statistics
• Financial Modeling

Background:

• Multivariate time series analysis is crucial for financial modeling.
• Accurate estimation of volatility and risk is essential for financial institutions.
• Existing methods for multivariate volatility estimation have limitations.

Purpose of the Study:

• To introduce Independent Component Analysis-Generalized Autoregressive Conditional Heteroskedasticity (ICA-GARCH) models.
• To enhance the estimation of multivariate volatilities.
• To improve the computation of value at risk (VaR) for risk management.

Main Methods:

• Decomposition of multivariate time series using Independent Component Analysis (ICA).
• Development and application of computationally efficient ICA-GARCH models.
• Comparative analysis against existing methods like DCC, PCA-GARCH, and EWMA.
• Validation through backtesting and out-of-sample performance evaluation.

Main Results:

• ICA-GARCH models demonstrate superior effectiveness in estimating multivariate volatilities compared to DCC, PCA-GARCH, and EWMA.
• The proposed models provide accurate and reliable estimation of Value at Risk (VaR).
• Experimental results confirm the practical utility of ICA-GARCH for risk management.

Conclusions:

• ICA-GARCH models offer a significant advancement in multivariate volatility modeling.
• The models provide a robust framework for financial risk management, particularly for VaR estimation.
• The proposed approach is computationally efficient and empirically validated.