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| Nichtlineares DCC-GARCH-Modell (Asymmetrische dynamische bedingte Korrelation)× | EGARCH-Modell (Exponential GARCH)× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2006 | 1991 |
| Urheber≠ | Cappiello, Engle & Sheppard | Daniel B. Nelson |
| Typ≠ | Multivariate volatility and correlation model | Volatility / conditional variance model |
| Wegweisende Quelle≠ | Cappiello, 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 ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Aliasnamen | ADCC-GARCH, Asymmetric DCC-GARCH, NL-DCC-GARCH, Nonlinear Asymmetric DCC | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Verwandt≠ | 2 | 6 |
| Zusammenfassung≠ | The 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 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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