Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo TGARCH (Threshold GARCH)× | Modelo ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1993-1994 | 1970 |
| Autor original≠ | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) | George Box and Gwilym Jenkins |
| Tipo≠ | Asymmetric volatility model | Time series forecasting model |
| Fuente seminal≠ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Relacionados | 6 | 6 |
| Resumen≠ | 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 ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateConjunto de datos ↗ |
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