Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Греки через автоматическое дифференцирование× | Модель Бейтса× | |
|---|---|---|
| Область | Количественные финансы | Количественные финансы |
| Семейство≠ | Machine learning | Regression model |
| Год появления≠ | 2008 | 1996 |
| Автор метода≠ | Mike Giles, Iman Homescu | David S. Bates |
| Тип≠ | Sensitivity Analysis | Equity/FX Model |
| Основополагающий источник≠ | Giles, M. B. (2008). Adjoint code by automatic differentiation. Journal of Computational Finance, 12(1), 1-18. link ↗ | Bates, D. S. (1996). Jumps and stochastic volatility: Exchange rate processes implicit in Deutsche Mark options. Review of Financial Studies, 9(1), 69-107. DOI ↗ |
| Другие названия≠ | AD Greeks, Algorithmic Differentiation, Autodiff | SVJ Model, Jump Diffusion |
| Связанные≠ | 3 | 4 |
| Сводка≠ | Automatic differentiation (AD) is a computational technique for computing derivatives (Greeks) by differentiating the computer code that computes the option price. AD avoids manual derivation of formulas and finite-difference approximations, yielding exact sensitivities with machine precision. It has become essential for real-time risk management in modern trading systems. | The Bates model (1996) combines stochastic volatility and jump diffusion to capture both the volatility smile and the implied volatility skew observed in equity and currency option markets. It extends the Heston model by adding a Poisson jump component to returns, making it suitable for pricing options when sudden price moves are expected. |
| ScholarGateНабор данных ↗ |
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