方法对比
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| 非线性TGARCH模型× | 自回归条件异方差 (ARCH) 模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1993–1994 | 1982 |
| 提出者≠ | Jean-Michel Zakoian; related work by Glosten, Jagannathan & Runkle | Robert F. Engle |
| 类型≠ | Conditional heteroskedasticity model | Conditional volatility model |
| 开创性文献≠ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| 别名 | NL-TGARCH, Nonlinear Threshold GARCH, Asymmetric TGARCH, GJR-GARCH variant | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| 相关≠ | 4 | 6 |
| 摘要≠ | The Nonlinear TGARCH (Threshold GARCH) model extends the standard GARCH framework by allowing positive and negative shocks of equal magnitude to exert different effects on future volatility. It models conditional volatility in terms of the absolute value of lagged residuals split by a sign threshold, capturing the well-documented leverage effect in financial return series. | The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering. |
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