Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Μοντέλο EGARCH (Exponential GARCH)× | Μοντέλο TGARCH (Threshold GARCH)× | |
|---|---|---|
| Πεδίο | Οικονομετρία | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 1991 | 1993-1994 |
| Δημιουργός≠ | Daniel B. Nelson | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) |
| Τύπος≠ | Volatility / conditional variance model | Asymmetric volatility model |
| Θεμελιώδης πηγή≠ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ |
| Εναλλακτικές ονομασίες | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH |
| Συναφείς | 6 | 6 |
| Σύνοψη≠ | 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. | The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|