Võrdle meetodeid

	Aegmuutuvate parameetritega TGARCH-mudel	Oleku ruum mudel (Kalmani filter)
Valdkond	Ökonomeetria	Ökonomeetria
Perekond	Regression model	Regression model
Tekkeaasta	1990s–2000s	1990
Looja	Extension combining Zakoïan (1994) TGARCH and time-varying parameter methods	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tüüp	Volatility model with asymmetry and parameter evolution	State space time series model
Algallikas	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.
Rööpnimetused	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)
Seotud	4	4
Kokkuvõte	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.