विधियों की तुलना करें

	वास्तविक अस्थिरता और HAR मॉडल	एक्सपोनेंशियल GARCH (EGARCH)	जोहान्सन सह-एकीकरण परीक्षण और सदिश त्रुटि सुधार मॉडल	दीर्घ-स्मृति मॉडल (ARFIMA, FIGARCH)
क्षेत्र	वित्त	अर्थमिति	वित्त	वित्त
परिवार	Regression model	Regression model	Regression model	Regression model
उद्भव वर्ष	2009	1991	1991	1980
प्रवर्तक	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)	Nelson	Søren Johansen	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)
प्रकार	Time-series regression of realized variance	Conditional volatility model (asymmetric GARCH variant)	Multivariate cointegration / vector error correction model	Fractionally integrated time series model
मौलिक स्रोत	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.	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.	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.
उपनाम	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	Johansen test, VECM, vector error correction model, multivariate cointegration	ARFIMA, FIGARCH, fractionally integrated models, fractional integration
संबंधित	5	4	3	4
सारांश	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.	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.	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.