Time varying correlations in financial data can be estimated using a new type of model called dynamic conditional correlation (DCC) models. These models combine the flexibility of univariate GARCH models with simple parametric models for correlations. DCC models are not linear but can be easily estimated using likelihood functions. They have been shown to perform well in various situations and provide reliable empirical results.