Regression modelEconometrics / time series

Time-Varying Parameter DCC-GARCH Model

The TVP-DCC-GARCH model extends the Dynamic Conditional Correlation GARCH framework by allowing not only the pairwise correlations but also the underlying model parameters to evolve continuously over time. It captures structural shifts in volatility dynamics and cross-asset dependence, making it essential for financial risk modelling in non-stationary environments.

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Sources

  1. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI: 10.1198/073500102288618487
  2. Christoffersen, P., Errunza, V., Jacobs, K., & Langlois, H. (2012). Is the potential for international diversification disappearing? A dynamic copula approach. Review of Financial Studies, 25(12), 3711-3751. DOI: 10.1093/rfs/hhs104

Related methods

ScholarGateTime-varying parameter DCC-GARCH model (Time-Varying Parameter Dynamic Conditional Correlation GARCH Model). Retrieved 2026-06-04 from https://scholargate.app/en/econometrics/time-varying-parameter-dcc-garch-model