Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модели риска ликвидности (Amihud, Roll, LOT)× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|
| Область≠ | Финансы | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2002 | 2019 |
| Автор метода≠ | Amihud (2002); Roll (1984); Lesmond, Ogden & Trzcinka (LOT) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Liquidity / illiquidity measurement models | Linear regression |
| Основополагающий источник≠ | Amihud, Y. (2002). Illiquidity and Stock Returns: Cross-Section and Time-Series Effects. Journal of Financial Markets, 5(1), 31-56. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Другие названия≠ | Amihud illiquidity, Roll spread estimator, LOT spread measure, Lesmond-Ogden-Trzcinka measure | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Связанные | 5 | 5 |
| Сводка≠ | Liquidity Risk Models are a family of measures that quantify how easily an asset trades by capturing its price impact, its effective bid-ask spread, and a holding-period adjustment. The family brings together the Amihud illiquidity ratio (Amihud, 2002), the Roll serial-covariance spread estimator (Roll, 1984), and the LOT (Lesmond-Ogden-Trzcinka) realised-spread measure. | 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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