Regression modelEconometrics / time series
贝叶斯 EGARCH 模型
贝叶斯 EGARCH 模型将 Nelson (1991) 的指数 GARCH (EGARCH) 规范——该规范对条件方差的对数进行建模并捕捉杠杆效应——与通过马尔可夫链蒙特卡洛 (MCMC) 进行的贝叶斯后验推断相结合。这允许对所有波动率参数（包括不对称系数）进行完全的不确定性量化，而无需估计量具有大样本正态性。
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来源
- Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI: 10.2307/2938260 ↗
- Nakatsuma, T. (2000). Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach. Journal of Econometrics, 95(1), 57–69. DOI: 10.1016/S0304-4076(99)00029-9 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Exponential Generalized Autoregressive Conditional Heteroscedasticity Model. ScholarGate. https://scholargate.app/zh/econometrics/bayesian-egarch
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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