Сравнение методов

	Модель ARIMA (авторегрессионная интегрированная скользящая средняя)	Квантильная регрессия	Реализованная волатильность и модель HAR
Область	Эконометрика	Эконометрика	Финансы
Семейство	Regression model	Regression model	Regression model
Год появления	2015	1978	2009
Автор метода	Box & Jenkins (Box-Jenkins methodology)	Koenker & Bassett	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
Тип	Univariate time-series model	Conditional quantile regression	Time-series regression of realized variance
Основополагающий источник	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	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
Другие названия	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
Связанные	5	5	5
Сводка	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).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.