Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Grații prin Diferențiere Automată× | Volatilitatea locală (Dupire)× | |
|---|---|---|
| Domeniu | Finanțe cantitative | Finanțe cantitative |
| Familie≠ | Machine learning | Regression model |
| Anul apariției≠ | 2008 | 1994 |
| Autorul original≠ | Mike Giles, Iman Homescu | Bruno Dupire |
| Tip≠ | Sensitivity Analysis | Equity/FX Model |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative≠ | AD Greeks, Algorithmic Differentiation, Autodiff | Deterministic Volatility Function, DVF |
| Înrudite≠ | 3 | 4 |
| Rezumat≠ | 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. |
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