方法对比
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| 贝叶斯阈值GARCH模型 (Bayesian TGARCH)× | 贝叶斯 EGARCH 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≠ | 1994 / 2008 | 1991 (EGARCH); 2000s (Bayesian estimation) |
| 提出者≠ | Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008) | Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s |
| 类型≠ | Volatility model with asymmetric threshold and Bayesian inference | Volatility model with Bayesian inference |
| 开创性文献≠ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| 别名 | Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B | Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH |
| 相关 | 6 | 6 |
| 摘要≠ | Bayesian TGARCH combines the Threshold GARCH volatility model — which captures the asymmetric response of volatility to positive versus negative shocks — with full Bayesian inference via Markov Chain Monte Carlo sampling. The result is a principled, uncertainty-aware framework for modeling leverage effects and fat-tailed financial returns. | The Bayesian EGARCH model combines Nelson's (1991) Exponential GARCH specification — which models the log of conditional variance and captures the leverage effect — with Bayesian posterior inference via Markov Chain Monte Carlo (MCMC). This allows full uncertainty quantification of all volatility parameters, including the asymmetry coefficient, without requiring large-sample normality of the estimates. |
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