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| 自动微分计算希腊值× | 局部波动率 (Dupire)× | |
|---|---|---|
| 领域 | 量化金融 | 量化金融 |
| 方法族≠ | Machine learning | Regression model |
| 起源年份≠ | 2008 | 1994 |
| 提出者≠ | Mike Giles, Iman Homescu | Bruno Dupire |
| 类型≠ | Sensitivity Analysis | Equity/FX Model |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | AD Greeks, Algorithmic Differentiation, Autodiff | Deterministic Volatility Function, DVF |
| 相关≠ | 3 | 4 |
| 摘要≠ | 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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