方法对比
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| 贝叶斯动态条件相关GARCH (Bayesian DCC-GARCH)× | 贝叶斯阈值GARCH模型 (Bayesian TGARCH)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2002 (DCC); 2000s (Bayesian extension) | 1994 / 2008 |
| 提出者≠ | Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards) | Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008) |
| 类型≠ | Multivariate volatility model | Volatility model with asymmetric threshold and Bayesian inference |
| 开创性文献≠ | Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ |
| 别名 | Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model | Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B |
| 相关 | 6 | 6 |
| 摘要≠ | Bayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maximising a likelihood, it places prior distributions over all parameters and uses Markov Chain Monte Carlo (MCMC) sampling to produce full posterior distributions, yielding richer uncertainty quantification than classical DCC-GARCH. | 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. |
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