Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Panel TGARCH (Threshold GARCH pentru Date Paneliu)× | DCC-GARCH (Dynamic Conditional Correlation)× | Panel EGARCH× | |
|---|---|---|---|
| Domeniu≠ | Econometrie | Finanțe | Econometrie |
| Familie | Regression model | Regression model | Regression model |
| Anul apariției≠ | 1993–1994 (panel extension: 2000s onward) | 2002 | 1991 (EGARCH); panel extensions widely used from 2000s |
| Autorul original≠ | Glosten, Jagannathan & Runkle (1993); Zakoian (1994); extended to panel settings by subsequent applied finance literature | Robert F. Engle | Daniel B. Nelson (EGARCH); panel extension by applied econometrics literature |
| Tip≠ | Asymmetric conditional volatility model | Multivariate volatility model | Volatility model |
| Sursa seminală≠ | 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. Journal of Finance, 48(5), 1779–1801. DOI ↗ | Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & 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 ↗ |
| Denumiri alternative | Panel GJR-GARCH, Panel Asymmetric GARCH, Panel Threshold GARCH, TGARCH panel model | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | Panel EGARCH model, panel exponential GARCH, EGARCH for panel data, cross-sectional EGARCH |
| Înrudite≠ | 4 | 5 | 4 |
| Rezumat≠ | Panel TGARCH extends the Threshold GARCH (GJR-GARCH) model to panel data, allowing each cross-sectional unit to exhibit asymmetric volatility responses — where negative shocks generate larger variance increases than positive shocks of the same magnitude — while exploiting the cross-sectional dimension to obtain more efficient parameter estimates. | 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. | Panel EGARCH extends Nelson's (1991) Exponential GARCH model to a panel setting, allowing conditional variance to evolve asymmetrically over time for each cross-sectional unit. The log specification ensures non-negative variance without parameter constraints, and the leverage term distinguishes whether negative shocks amplify volatility more than positive ones of equal magnitude. |
| ScholarGateSet de date ↗ |
|
|
|