Adaptive Dynamic Conditional Correlation Model for Financial Markets
About this article
Keywords:
Adaptive DCC, Dynamic Correlations, GARCH Model, Risk Forecasting, Time-Varying Parameters, Asymmetric ShocksAbstract
This paper presents the Adaptive Dynamic Conditional Correlation (AD-DCC) model, an advanced multivariate framework that extends the standard DCC model by incorporating time-varying parameters to capture asymmetric and rapid shifts in asset correlations, ensuring a valid correlation matrix. The model employs a two step estimation process, modeling asset volatilities with univariate GARCH models followed by adaptive correlation dynamics responsive to market conditions. It is applied to daily returns of equities, bonds, commodities, and currencies from 2010 to 2024, capturing complex time-varying correlation patterns. Empirical results show that the AD-DCC model outperforms benchmark models, particularly in volatile markets, enhancing portfolio optimization and risk forecasting accuracy.
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