Salīdzināt metodes

	HAR-RV model of realized volatility	Parastā mazāko kvadrātu (OLS) regresija
Nozare	Finanses	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2009	2019
Autors	Fulvio Corsi	Wooldridge (textbook treatment); classical least squares
Tips	Linear time-series regression for volatility	Linear regression
Pirmavots	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Citi nosaukumi	HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Saistītās	5	5
Kopsavilkums	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).