Salīdzināt metodes

	GARCH modelis (volatilitātes prognozēšana)	HAR-RV model of realized volatility
Nozare	Ekonometrija	Finanses
Saime	Regression model	Regression model
Izcelsmes gads	1986	2009
Autors	Tim Bollerslev	Fulvio Corsi
Tips	Conditional volatility model	Linear time-series regression for volatility
Pirmavots	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196.
Citi nosaukumi	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility)
Saistītās	5	5
Kopsavilkums	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.	The HAR-RV model, introduced by Fulvio Corsi in 2009, forecasts realized volatility by decomposing it into daily, weekly, and monthly components. It is a simple linear regression that mirrors how market participants with different investment horizons react to volatility, and it naturally captures the long-memory behaviour of volatility.