Model EGARCH Bayesian
Model EGARCH Bayesian menggabungkan spesifikasi Exponential GARCH (EGARCH) Nelson (1991) — yang memodelkan log varians bersyarat dan menangkap kesan leverage — dengan inferens posterior Bayesian melalui Markov Chain Monte Carlo (MCMC). Ini memungkinkan kuantifikasi ketidakpastian penuh untuk semua parameter volatiliti, termasuk pekali asimetri, tanpa memerlukan normaliti anggaran sampel besar.
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Method map
The neighbourhood of related methods — select a node to explore.
Sumber
- 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 ↗
Cara memetik halaman ini
ScholarGate. (2026, June 3). Bayesian Exponential Generalized Autoregressive Conditional Heteroscedasticity Model. ScholarGate. https://scholargate.app/ms/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.
- Model ARCH (Heteroskedastisitas Bersyarat Autoregresif)Ekonometrik↔ compare
- Bayesian Dynamic Conditional Correlation GARCH (Bayesian DCC-GARCH)Ekonometrik↔ compare
- Model GARCH BayesianEkonometrik↔ compare
- TGARCH Bayesian (Threshold GARCH dengan Anggaran Bayesian)Ekonometrik↔ compare
- Model VAR Bayesian (BVAR)Ekonometrik↔ compare
- Model EGARCH (Exponential GARCH)Ekonometrik↔ compare
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