Sammenlign metoder

	ARIMA (Autoregressive Integrated Moving Average) Modell	Eksponentiell GARCH (EGARCH)	Kvantilregresjon	Realisert volatilitet og HAR-modellen
Fagfelt	Økonometri	Økonometri	Økonometri	Finans
Familie	Regression model	Regression model	Regression model	Regression model
Opprinnelsesår	2015	1991	1978	2009
Opphavsperson	Box & Jenkins (Box-Jenkins methodology)	Nelson	Koenker & Bassett	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
Type	Univariate time-series model	Conditional volatility model (asymmetric GARCH variant)	Conditional quantile regression	Time-series regression of realized variance
Opprinnelig kilde	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.	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.
Alias	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	conditional quantile regression, regression quantiles, Kantil Regresyon	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
Relaterte	5	4	5	5
Sammendrag	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.	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.