Comparer des méthodes

	Modèle TGARCH à paramètres variant dans le temps	Modèle d'espace d'états (Filtre de Kalman)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	1990
Auteur d'origine	Extension combining Zakoïan (1994) TGARCH and time-varying parameter methods	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Type	Volatility model with asymmetry and parameter evolution	State space time series model
Source fondatrice	Zakoïan, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Alias	TVP-TGARCH, time-varying TGARCH, threshold GARCH with time-varying parameters, TVP Threshold GARCH	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Apparentées	4	4
Résumé	The TVP-TGARCH model extends Threshold GARCH by allowing its volatility parameters to evolve over time via a state-space representation. It captures both the leverage effect — that negative return shocks increase volatility more than positive ones — and structural change in that asymmetry, making it well-suited for long financial time series subject to regime shifts.	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.