Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modelis Panel GARCH× | EGARCH modelis (eksponenciālais GARCH)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 (GARCH); panel extension 1990s–2000s | 1991 |
| Autors≠ | Bollerslev (1986); extended to panel settings in subsequent literature | Daniel B. Nelson |
| Tips≠ | Volatility model | Volatility / conditional variance model |
| Pirmavots≠ | Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Citi nosaukumi | panel GARCH, GARCH panel model, panel volatility model, panel conditional heteroscedasticity model | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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 Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets. |
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