Comparer des méthodes

	Modèle GARCH non linéaire	Autoregressive Vectoriel (VAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1991-1993	1980
Auteur d'origine	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	Christopher A. Sims
Type	Volatility model	Multivariate time-series model
Source fondatrice	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.	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.
Alias	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	VAR, VAR model, vector autoregressive model, multivariate autoregression
Apparentées	6	5
Résumé	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.