方法对比

	已实现波动率与HAR模型	ARIMA（自回归积分滑动平均）模型	Johansen协整检验与向量误差修正模型
领域	金融学	计量经济学	金融学
方法族	Regression model	Regression model	Regression model
起源年份	2009	2015	1991
提出者	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)	Box & Jenkins (Box-Jenkins methodology)	Søren Johansen
类型	Time-series regression of realized variance	Univariate time-series model	Multivariate cointegration / vector error correction model
开创性文献	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	Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580.
别名	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Johansen test, VECM, vector error correction model, multivariate cointegration
相关	5	5	3
摘要	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).	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.