เปรียบเทียบวิธี

	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	ความผันผวนที่รับรู้ได้และแบบจำลอง HAR
สาขาวิชา	เศรษฐมิติ	การเงิน
ตระกูล	Regression model	Regression model
ปีกำเนิด	2015	2009
ผู้ริเริ่ม	Box & Jenkins (Box-Jenkins methodology)	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
ประเภท	Univariate time-series model	Time-series regression of realized variance
แหล่งต้นตำรับ	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	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
ชื่อเรียกอื่น	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
ที่เกี่ยวข้อง	5	5
สรุป	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).	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.