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Model DCC-GARCH (dynamická podmienená korelácia)×Model EGARCH (Exponenciálny GARCH)×
OdborEkonometriaEkonometria
RodinaRegression modelRegression model
Rok vzniku20021991
TvorcaRobert F. EngleDaniel B. Nelson
TypMultivariate volatility modelVolatility / conditional variance model
Pôvodný zdrojEngle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI ↗Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗
Ďalšie názvyDCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCCExponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Príbuzné56
ZhrnutieThe DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.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: DCC-GARCH model · EGARCH model. Získané 2026-06-18 z https://scholargate.app/sk/compare