Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Panel GARCH-model× | DCC-GARCH Model (Dynamic Conditional Correlation)× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1986 (GARCH); panel extension 1990s–2000s | 2002 |
| Grondlegger≠ | Bollerslev (1986); extended to panel settings in subsequent literature | Robert F. Engle |
| Type≠ | Volatility model | Multivariate volatility model |
| Oorspronkelijke bron≠ | Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. 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 ↗ |
| Aliassen | panel GARCH, GARCH panel model, panel volatility model, panel conditional heteroscedasticity model | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Verwant≠ | 6 | 5 |
| Samenvatting≠ | The Panel GARCH model extends Bollerslev's (1986) Generalized Autoregressive Conditional Heteroscedasticity framework to panel data, allowing conditional variance to evolve over time for each cross-sectional unit. It simultaneously captures unit-level heterogeneity and time-varying volatility clustering, making it the standard tool for modelling risk and uncertainty in multi-entity financial and macroeconomic panels. | 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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