Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo EGARCH (GARCH Exponencial)× | Modelo ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1991 | 1970 |
| Autor original≠ | Daniel B. Nelson | George Box and Gwilym Jenkins |
| Tipo≠ | Volatility / conditional variance model | Time series forecasting model |
| Fuente seminal≠ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Relacionados | 6 | 6 |
| Resumen≠ | 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 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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