方法对比
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| 贝叶斯GARCH模型× | GARCH 模型（波动率预测）× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1989–2000 | 1986 |
| 提出者≠ | Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998) | Tim Bollerslev |
| 类型≠ | Bayesian volatility model | Conditional volatility model |
| 开创性文献≠ | Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| 别名 | Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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. | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. |
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