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| Изчисляване на гръцките букви чрез автоматично диференциране× | Локална волатилност (Дюпир)× | |
|---|---|---|
| Област | Количествени финанси | Количествени финанси |
| Семейство≠ | Machine learning | Regression model |
| Година на възникване≠ | 2008 | 1994 |
| Създател≠ | Mike Giles, Iman Homescu | Bruno Dupire |
| Тип≠ | Sensitivity Analysis | Equity/FX Model |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | AD Greeks, Algorithmic Differentiation, Autodiff | Deterministic Volatility Function, DVF |
| Свързани≠ | 3 | 4 |
| Резюме≠ | 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 от данни ↗ |
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