Σύγκριση μεθόδων

	Πραγματισμένη Μεταβλητότητα και το Μοντέλο HAR	Μοντέλα Μακράς Μνήμης (ARFIMA, FIGARCH)
Πεδίο	Χρηματοοικονομικά	Χρηματοοικονομικά
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2009	1980
Δημιουργός	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)
Τύπος	Time-series regression of realized variance	Fractionally integrated time series model
Θεμελιώδης πηγή	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.	Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29.
Εναλλακτικές ονομασίες	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV	ARFIMA, FIGARCH, fractionally integrated models, fractional integration
Συναφείς	5	4
Σύνοψη	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.	Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration.