Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika mainīgo parametru TGARCH modelis× | EGARCH modelis (eksponenciālais GARCH)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990s–2000s | 1991 |
| Autors≠ | Extension combining Zakoïan (1994) TGARCH and time-varying parameter methods | Daniel B. Nelson |
| Tips≠ | Volatility model with asymmetry and parameter evolution | Volatility / conditional variance model |
| Pirmavots≠ | Zakoïan, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Citi nosaukumi | TVP-TGARCH, time-varying TGARCH, threshold GARCH with time-varying parameters, TVP Threshold GARCH | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Saistītās≠ | 4 | 6 |
| Kopsavilkums≠ | The TVP-TGARCH model extends Threshold GARCH by allowing its volatility parameters to evolve over time via a state-space representation. It captures both the leverage effect — that negative return shocks increase volatility more than positive ones — and structural change in that asymmetry, making it well-suited for long financial time series subject to regime shifts. | 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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