Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| DCC-GARCH (Dynamic Conditional Correlation)× | GJR-GARCH (GARCH Asimetric)× | |
|---|---|---|
| Domeniu≠ | Finanțe | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2002 | 1993 |
| Autorul original≠ | Robert F. Engle | Glosten, Jagannathan & Runkle (1993); Zakoian (1994) |
| Tip≠ | Multivariate volatility model | Asymmetric conditional volatility model |
| Sursa seminală≠ | Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350. DOI ↗ | Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801. DOI ↗ |
| Denumiri alternative | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle) |
| Înrudite | 5 | 5 |
| Rezumat≠ | DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step. | GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994). |
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