Comparer des méthodes

	Modèle GARCH à Paramètres Variables dans le Temps (TVP-GARCH)	Modèle d'espace d'états (Filtre de Kalman)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1982–2013	1990
Auteur d'origine	Engle (1982) for ARCH/GARCH foundation; extended by Creal, Koopman & Lucas (2013) and others for time-varying parameter variants	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Type	Volatility model with time-varying coefficients	State space time series model
Source fondatrice	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Alias	TVP-GARCH, time-varying GARCH, TV-GARCH, state-space GARCH	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Apparentées	5	4
Résumé	The Time-Varying Parameter GARCH model extends the standard GARCH framework by allowing the conditional variance parameters — including the ARCH and GARCH coefficients — to change over time rather than remaining fixed throughout the sample. This makes it well-suited to financial and macroeconomic series where volatility dynamics evolve across different market regimes or economic episodes.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.