Σύγκριση μεθόδων

	Μοντέλο ARIMA (Autoregressive Integrated Moving Average)	Εκθετικό GARCH (EGARCH)	Δοκιμή Συνολοκλήρωσης κατά Johansen και Μοντέλο Διόρθωσης Σφαλμάτων Διανυσμάτων
Πεδίο	Οικονομετρία	Οικονομετρία	Χρηματοοικονομικά
Οικογένεια	Regression model	Regression model	Regression model
Έτος προέλευσης	2015	1991	1991
Δημιουργός	Box & Jenkins (Box-Jenkins methodology)	Nelson	Søren Johansen
Τύπος	Univariate time-series model	Conditional volatility model (asymmetric GARCH variant)	Multivariate cointegration / vector error correction model
Θεμελιώδης πηγή	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	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580.
Εναλλακτικές ονομασίες	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	Johansen test, VECM, vector error correction model, multivariate cointegration
Συναφείς	5	4	3
Σύνοψη	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).	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.	The Johansen procedure is a multivariate cointegration framework, introduced by Søren Johansen in 1991, that tests for long-run equilibrium relationships among several I(1) time series. It determines how many cointegrating vectors link the series and then builds a Vector Error Correction Model (VECM) to describe the short-run dynamics around that equilibrium.