Compară metode

	GARCH Exponențial (EGARCH)	Regresia cuantilică	Volatilitatea Realizată și Modelul HAR
Domeniu	Econometrie	Econometrie	Finanțe
Familie	Regression model	Regression model	Regression model
Anul apariției	1991	1978	2009
Autorul original	Nelson	Koenker & Bassett	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
Tip	Conditional volatility model (asymmetric GARCH variant)	Conditional quantile regression	Time-series regression of realized variance
Sursa seminală	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
Denumiri alternative	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	conditional quantile regression, regression quantiles, Kantil Regresyon	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
Înrudite	4	5	5
Rezumat	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction.