Salīdzināt metodes

	Laika mainīgo parametru DCC-GARCH modelis	Dinamiskais faktormodelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2002 (DCC-GARCH); TVP extension 2010s	2002
Autors	Robert F. Engle (DCC-GARCH); TVP extension developed in applied finance literature	James Stock & Mark Watson
Tips	Multivariate volatility model with time-varying correlation	Latent-factor time-series model
Pirmavots	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.	Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147–162.
Citi nosaukumi	TVP-DCC-GARCH, time-varying DCC-GARCH, dynamic conditional correlation GARCH with TVP, TVP dynamic conditional correlation model	Diffusion Index Model, Large-Scale Factor Model, Approximate Factor Model, Dinamik Faktör Modeli
Saistītās	4	2
Kopsavilkums	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.	A Dynamic Factor Model (DFM) extracts a small number of latent common factors from a large panel of economic time series and uses those factors to forecast or nowcast a target variable. Formalized for macroeconomic forecasting by James Stock and Mark Watson in their 2002 Journal of Business & Economic Statistics paper, DFMs handle hundreds of indicators simultaneously while avoiding the curse of dimensionality that plagues traditional multivariate models.