Σύγκριση μεθόδων

	GJR-GARCH (Ασύμμετρο GARCH)	Μοντέλο ARCH (Αυτοπαλίνδρομη Συνθηκική Ετεροσκεδαστικότητα)	Μοντέλο ARIMA (Autoregressive Integrated Moving Average)	Μοντέλο GARCH (Πρόβλεψη Μεταβλητότητας)
Πεδίο	Οικονομετρία	Οικονομετρία	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model	Regression model	Regression model
Έτος προέλευσης	1993	1982	2015	1986
Δημιουργός	Glosten, Jagannathan & Runkle (1993); Zakoian (1994)	Robert F. Engle	Box & Jenkins (Box-Jenkins methodology)	Tim Bollerslev
Τύπος	Asymmetric conditional volatility model	Conditional volatility model	Univariate time-series model	Conditional volatility model
Θεμελιώδης πηγή	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. The Journal of Finance, 48(5), 1779-1801.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Εναλλακτικές ονομασίες	asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Συναφείς	5	6	5	5
Σύνοψη	GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994).	The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.