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| DCC-GARCH× | GJR-GARCH (aszimmetrikus GARCH)× | Paneladatok rögzített hatású modellje× | |
|---|---|---|---|
| Tudományterület≠ | Pénzügy | Ökonometria | Ökonometria |
| Módszercsalád | Regression model | Regression model | Regression model |
| Keletkezés éve≠ | 2002 | 1993 | 2014 |
| Megalkotó≠ | Robert F. Engle | Glosten, Jagannathan & Runkle (1993); Zakoian (1994) | Hsiao (textbook treatment); within transformation of panel data |
| Típus≠ | Multivariate volatility model | Asymmetric conditional volatility model | Panel data regression |
| Alapmű≠ | 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 ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Alternatív nevek | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle) | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Kapcsolódó | 5 | 5 | 5 |
| Összefoglaló≠ | 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). | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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