方法对比
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| 贝叶斯阈值GARCH模型 (Bayesian TGARCH)× | 贝叶斯GARCH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 | 1989–2000 |
| 提出者≠ | Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008) | Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998) |
| 类型≠ | Volatility model with asymmetric threshold and Bayesian inference | Bayesian 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 ↗ | Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86. DOI ↗ |
| 别名 | Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B | Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model |
| 相关≠ | 6 | 4 |
| 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 GARCH model combines the GARCH framework for time-varying volatility with Bayesian posterior inference. Instead of maximising a likelihood, it specifies prior distributions for the GARCH parameters and draws from the resulting posterior — typically via Markov chain Monte Carlo (MCMC) — to quantify both point estimates and full uncertainty about volatility dynamics. |
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