So sánh phương pháp

	Biến động thực hiện và Mô hình HAR	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Exponential GARCH (EGARCH)	Mô hình bộ nhớ dài (ARFIMA, FIGARCH)
Lĩnh vực	Tài chính	Kinh tế lượng	Kinh tế lượng	Tài chính
Họ	Regression model	Regression model	Regression model	Regression model
Năm ra đời	2009	2015	1991	1980
Người khởi xướng	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)	Box & Jenkins (Box-Jenkins methodology)	Nelson	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)
Loại	Time-series regression of realized variance	Univariate time-series model	Conditional volatility model (asymmetric GARCH variant)	Fractionally integrated time series model
Công trình gốc	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.	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.	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.
Tên gọi khác	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	ARFIMA, FIGARCH, fractionally integrated models, fractional integration
Liên quan	5	5	4	4
Tóm tắt	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.	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.	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.