Сравнение методов

	DCC-GARCH модель с изменяющимися во времени параметрами (TVP-DCC-GARCH)	Динамическая факторная модель
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	2002 (DCC-GARCH); TVP extension 2010s	2002
Автор метода	Robert F. Engle (DCC-GARCH); TVP extension developed in applied finance literature	James Stock & Mark Watson
Тип	Multivariate volatility model with time-varying correlation	Latent-factor time-series model
Основополагающий источник	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.
Другие названия	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
Связанные	4	2
Сводка	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.