Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел HAR-RV на реализираната волатилност× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Финанси | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2009 | 2019 |
| Създател≠ | Fulvio Corsi | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Linear time-series regression for volatility | Linear regression |
| Основополагащ източник≠ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия | 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 |
| Свързани | 5 | 5 |
| Резюме≠ | 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). |
| ScholarGateНабор от данни ↗ |
|
|