方法对比
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| 贝叶斯GARCH模型× | 随机波动率模型 (Heston)× | |
|---|---|---|
| 领域≠ | 计量经济学 | 金融学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1989–2000 | 1993 |
| 提出者≠ | Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998) | Steven L. Heston |
| 类型≠ | Bayesian volatility model | Continuous-time stochastic 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 ↗ | Heston, S. L. (1993). A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. Review of Financial Studies, 6(2), 327-343. DOI ↗ |
| 别名 | Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model | Heston model, SV model, continuous-time stochastic volatility, Stokastik Volatilite Modeli (Heston, SV) |
| 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 stochastic volatility model is a continuous-time option-pricing and risk framework in which volatility follows its own random process rather than staying constant. The Heston model, introduced by Steven Heston in 1993, gives the variance a mean-reverting square-root (CIR) dynamic and yields a closed-form option price; it is the continuous-time counterpart of GARCH. |
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