Comparar métodos

	Volatilidade Realizada e o Modelo HAR	Exponential GARCH (EGARCH)	Modelos de Memória Longa (ARFIMA, FIGARCH)
Área	Finanças	Econometria	Finanças
Família	Regression model	Regression model	Regression model
Ano de origem	2009	1991	1980
Autor original	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)	Nelson	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)
Tipo	Time-series regression of realized variance	Conditional volatility model (asymmetric GARCH variant)	Fractionally integrated time series model
Fonte seminal	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.	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.
Outros nomes	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	ARFIMA, FIGARCH, fractionally integrated models, fractional integration
Relacionados	5	4	4
Resumo	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.	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.