方法对比
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| 贝叶斯动态条件相关GARCH (Bayesian DCC-GARCH)× | 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≠ | 2002 (DCC); 2000s (Bayesian extension) | 1989–2000 |
| 提出者≠ | Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards) | Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998) |
| 类型≠ | Multivariate volatility model | Bayesian volatility model |
| 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 ↗ | Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86. DOI ↗ |
| 别名 | Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model | Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model |
| 相关≠ | 6 | 4 |
| 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. | 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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