Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Греки через автоматическое дифференцирование× | Локальная волатильность (Dupire)× | |
|---|---|---|
| Область | Количественные финансы | Количественные финансы |
| Семейство≠ | Machine learning | Regression model |
| Год появления≠ | 2008 | 1994 |
| Автор метода≠ | Mike Giles, Iman Homescu | Bruno Dupire |
| 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 ↗ | Dupire, B. (1994). Pricing with a smile. Risk Magazine, 7(1), 18-20. link ↗ |
| Другие названия≠ | AD Greeks, Algorithmic Differentiation, Autodiff | Deterministic Volatility Function, DVF |
| 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. | Dupire's local volatility model (1994) is a deterministic framework that extracts a term and strike-dependent volatility function from market option prices. Unlike constant volatility, local volatility perfectly fits the observed implied volatility smile and is implemented via finite difference methods for European and American option pricing. |
| ScholarGateНабор данных ↗ |
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