GARCH and Multivariate GARCH Models Source Code Toolbox

We provide a comprehensive source code toolbox for implementing GARCH and multivariate GARCH models using MATLAB programming. Our toolbox incorporates advanced functionalities including maximum likelihood parameter estimation with numerical optimization algorithms, model diagnostics through residual analysis and volatility tests, and multi-step forecasting procedures with confidence intervals. The implementation features key MATLAB functions such as garchfit() for model calibration, garchpred() for volatility forecasting, and multivariate extensions like CCC-GARCH and DCC-GARCH architectures. Additionally, we provide detailed documentation with code examples demonstrating model specification, likelihood function implementation, and backtesting methodologies. This toolbox supports various financial analysis applications including equity volatility modeling, futures pricing, and foreign exchange risk management. Our commitment is to deliver robust computational tools that enable users to perform sophisticated market analysis and develop data-driven investment strategies through proper volatility modeling and risk assessment techniques.