Bayesiansk EGARCH-model
Den Bayesianske EGARCH-model kombinerer Nelson's (1991) Exponential GARCH-specifikation – som modellerer logaritmen af den betingede varians og fanger leverage-effekten – med Bayesiansk posterior inferens via Markov Chain Monte Carlo (MCMC). Dette muliggør fuld usikkerhedskvantificering af alle volatilitetsparametre, inklusive asymmetrikoefficienten, uden at kræve normalitet af estimaterne i store stikprøver.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Bayesian Exponential Generalized Autoregressive Conditional Heteroscedasticity Model. ScholarGate. https://scholargate.app/da/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.
- ARCH-model (Autoregressiv Betinget Heteroskedasticitet)Økonometri↔ compare
- Bayesiansk Dynamisk Betinget Korrelations-GARCH (Bayesiansk DCC-GARCH)Økonometri↔ compare
- Bayesiansk GARCH-modelØkonometri↔ compare
- Bayesian TGARCH (Threshold GARCH med Bayesiansk Estimering)Økonometri↔ compare
- Bayesiansk VAR-model (BVAR)Økonometri↔ compare
- EGARCH-model (Eksponentiel GARCH)Økonometri↔ compare
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