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Bayesovský GARCH model×Model EGARCH (Exponenciálny GARCH)×
OdborEkonometriaEkonometria
RodinaRegression modelRegression model
Rok vzniku1989–20001991
TvorcaGeweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)Daniel B. Nelson
TypBayesian volatility modelVolatility / conditional variance model
Pôvodný zdrojGeweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86. DOI ↗Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗
Ďalšie názvyBayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility modelExponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Príbuzné46
ZhrnutieThe 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 Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.
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ScholarGatePorovnať metódy: Bayesian GARCH model · EGARCH model. Získané 2026-06-17 z https://scholargate.app/sk/compare