Bayesiansk GARCH-model
Den Bayesianske GARCH-model kombinerer GARCH-rammeværket for tidsvarierende volatilitet med Bayesiansk posterior inferens. I stedet for at maksimere en likelihood specificeres prior-fordelinger for GARCH-parametrene, og der trækkes fra den resulterende posterior — typisk via Markov chain Monte Carlo (MCMC) — for at kvantificere både punktestimater og fuld usikkerhed om volatilitetsdynamikker.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86. DOI: 10.1016/0304-4076(89)90030-4 ↗
- 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 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Bayesian Generalized Autoregressive Conditional Heteroskedasticity Model. ScholarGate. https://scholargate.app/da/econometrics/bayesian-garch-model
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.
- ARCH-model (Autoregressiv Betinget Heteroskedasticitet)Økonometri↔ compare
- EGARCH-model (Eksponentiel GARCH)Økonometri↔ compare
- GARCH-model (volatilitetsprognoser)Økonometri↔ compare
- Stokastisk volatilitetsmodel (Heston)Finansiering↔ compare
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