Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| TGARCH-malli (Threshold GARCH)× | GARCH-malli (volatiliteetin ennustaminen)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1993-1994 | 1986 |
| Kehittäjä≠ | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) | Tim Bollerslev |
| Tyyppi≠ | Asymmetric volatility model | Conditional volatility model |
| Alkuperäislähde≠ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Rinnakkaisnimet | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | 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. | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. |
| ScholarGateAineisto ↗ |
|
|