Dynamic Conditional Correlation (DCC) GARCH Model
Advanced multivariate volatility model for portfolio analysis
Model Configuration
For optimal performance, we recommend 2-5 assets.
No analysis results yet
Select at least 2 tickers and click "Run DCC-GARCH Model" to start.
Running a multivariate model may take up to a minute.
Volatility Analysis
Asset Returns and Forecasted Volatility
Volatility Statistics
Forecast Horizon
Days ahead: -
Period: -
Model Configuration
GARCH(p,q): -
DCC(p,q): -
About the DCC-GARCH Model
The Dynamic Conditional Correlation GARCH (DCC-GARCH) model, introduced by Engle (2002), extends univariate GARCH models to a multivariate setting by allowing for time-varying correlations between assets.
Model Structure
The DCC-GARCH model is a two-step approach:
- Univariate GARCH: Each asset's volatility is modeled independently using standard GARCH models.
- Dynamic Correlation: The correlations between standardized residuals are modeled using a separate dynamic process.
The conditional covariance matrix Ht is decomposed as:
Ht = DtRtDt
Where:
- Dt is a diagonal matrix of volatilities from univariate GARCH models
- Rt is the time-varying correlation matrix