Financial Econometrics: Estimating MGARCH Models (DCC, CCC) скачать в хорошем качестве

Financial Econometrics: Estimating MGARCH Models (DCC, CCC)

📝 Overview: This video presents core insights on Multivariate Garch in Stata: Modeling Volatility & Dynamic Correlations. We delve into hypothesis analysis and implementation methodologies. 📊 Detailed Content: Multivariate GARCH in Stata are essential tools for analyzing comovement between time series, allowing for the modeling of both time-varying conditional means and conditional covariances,. The main challenge of the general MGARCH model is the excessive number of parameters; therefore, Stata provides four parsimonious parameterizations to balance flexibility and estimability: Diagonal vech (DVECH), Constant Conditional Correlation (CCC), Dynamic Conditional Correlation (DCC), and Varying Conditional Correlation (VCC). Among these, the DCC model (mgarch dcc) is often preferred because it allows conditional quasicorrelations to vary over time following a GARCH-like structure, offering more flexibility than the CCC model while requiring fewer parameters than the DVECH model. The implementation process begins with the mgarch command followed by the desired model type. For example, the syntax mgarch dcc (y1 y2), arch(1) garch(1) estimates a bivariate DCC model with univariate GARCH(1,1) structures for each series. Stata performs estimation using maximum likelihood (ML) or quasimaximum likelihood (QML) methods. Users can change the error distribution assumption from multivariate normal (the default) to multivariate Student’s t via the distribution(t) option to handle fat-tailed financial data. Following estimation, the predict command offers dynamic forecasts for conditional variances and correlations, significantly aiding in portfolio risk analysis