Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo GARCH no lineal× | Vector Autoregression (VAR)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1991-1993 | 1980 |
| Autor original≠ | Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH | Christopher A. Sims |
| Tipo≠ | Volatility model | Multivariate time-series model |
| Fuente seminal≠ | Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801. DOI ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| Alias | NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets. | Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance. |
| ScholarGateConjunto de datos ↗ |
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