Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| GARCH-MIDAS× | VAR Cuantil× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2012 | 2006 |
| Autor original≠ | Engle and Ghysels | Koenker and Xiao |
| Tipo≠ | Time-varying variance model | Distribution impulse response |
| Fuente seminal≠ | Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391. link ↗ | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ |
| Alias | Mixed-frequency volatility model | Quantile-based impulse response |
| Relacionados | 3 | 3 |
| Resumen≠ | GARCH-MIDAS decomposes volatility into short-term (GARCH) and long-term (MIDAS) components, allowing low-frequency macroeconomic variables to drive medium-term volatility while high-frequency returns govern daily fluctuations. Introduced by Engle and Ghysels (2012), this framework elegantly separates volatility time scales. The approach is powerful for understanding how macro conditions (growth, inflation) drive risk premia and for improved volatility forecasting. | Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times. |
| ScholarGateConjunto de datos ↗ |
|
|