Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robust EGARCH modelis× | EGARCH modelis (eksponenciālais GARCH)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2008 | 1991 |
| Autors≠ | Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors | Daniel B. Nelson |
| Tips≠ | Robust volatility model | Volatility / conditional variance model |
| Pirmavots≠ | Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Citi nosaukumi | Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect. | 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. |
| ScholarGateDatu kopa ↗ |
|
|