Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel EGARCH× | Paneļa DCC-GARCH modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 (EGARCH); panel extensions widely used from 2000s | 2002 |
| Autors≠ | Daniel B. Nelson (EGARCH); panel extension by applied econometrics literature | Robert F. Engle |
| Tips≠ | Volatility model | Multivariate volatility model |
| Pirmavots≠ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ | 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 ↗ |
| Citi nosaukumi | Panel EGARCH model, panel exponential GARCH, EGARCH for panel data, cross-sectional EGARCH | DCC-GARCH panel, panel dynamic conditional correlation, multivariate DCC-GARCH, Panel DCC |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. | 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. |
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