Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modelul Panel DCC-GARCH× | Modelul Panel GARCH× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2002 | 1986 (GARCH); panel extension 1990s–2000s |
| Autorul original≠ | Robert F. Engle | Bollerslev (1986); extended to panel settings in subsequent literature |
| Tip≠ | Multivariate volatility model | Volatility model |
| Sursa seminală≠ | Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroscedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI ↗ | Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Denumiri alternative | DCC-GARCH panel, panel dynamic conditional correlation, multivariate DCC-GARCH, Panel DCC | panel GARCH, GARCH panel model, panel volatility model, panel conditional heteroscedasticity model |
| Înrudite≠ | 5 | 6 |
| Rezumat≠ | The Panel DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation GARCH framework to panel data settings, jointly modelling time-varying volatility and cross-sectional correlations across multiple units (countries, firms, or assets) over time. It allows pairwise correlations to vary dynamically in response to market shocks while preserving parsimony via a two-step estimation. | 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. |
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