Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель GARCH (прогнозирование волатильности)× | Высокочастотные данные и анализ микроструктуры рынка× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|---|
| Область≠ | Эконометрика | Финансы | Эконометрика |
| Семейство | Regression model | Regression model | Regression model |
| Год появления≠ | 1986 | 2007 | 2019 |
| Автор метода≠ | Tim Bollerslev | Hasbrouck (2007); Aït-Sahalia & Jacod (2014) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Conditional volatility model | Market microstructure / high-frequency econometrics | Linear regression |
| Основополагающий источник≠ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Другие названия | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) | market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Связанные | 5 | 5 | 5 |
| Сводка≠ | 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. | Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014). | 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 данных ↗ |
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