方法对比
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| 时变参数TGARCH模型× | 状态空间模型（卡尔曼滤波器）× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1990s–2000s | 1990 |
| 提出者≠ | Extension combining Zakoïan (1994) TGARCH and time-varying parameter methods | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| 类型≠ | Volatility model with asymmetry and parameter evolution | State space time series model |
| 开创性文献≠ | Zakoïan, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| 别名 | 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) |
| 相关 | 4 | 4 |
| 摘要≠ | 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. |
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