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Nelinearni DCC-GARCH model (asimetrična dinamička uslovna korelacija)×DCC-GARCH model (dinamička uslovna korelacija)×
OblastEkonometrijaEkonometrija
PorodicaRegression modelRegression model
Godina nastanka20062002
TvoracCappiello, Engle & SheppardRobert F. Engle
TipMultivariate volatility and correlation modelMultivariate volatility model
Temeljni izvorCappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572. DOI ↗Engle, 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 ↗
Drugi naziviADCC-GARCH, Asymmetric DCC-GARCH, NL-DCC-GARCH, Nonlinear Asymmetric DCCDCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Srodne25
SažetakThe Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006), it is the standard tool for measuring time-varying co-movement and contagion effects in multivariate financial time series when bad news is expected to increase correlations more than good news.The 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.
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ScholarGateUporedite metode: Nonlinear DCC-GARCH model · DCC-GARCH model. Preuzeto 2026-06-18 sa https://scholargate.app/sr/compare