विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| स्वचालित विभेदन (AD) द्वारा ग्रीक्स की गणना× | स्थानीय अस्थिरता (Dupire)× | |
|---|---|---|
| क्षेत्र | मात्रात्मक वित्त | मात्रात्मक वित्त |
| परिवार≠ | Machine learning | Regression model |
| उद्भव वर्ष≠ | 2008 | 1994 |
| प्रवर्तक≠ | Mike Giles, Iman Homescu | Bruno Dupire |
| प्रकार≠ | Sensitivity Analysis | Equity/FX Model |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम≠ | AD Greeks, Algorithmic Differentiation, Autodiff | Deterministic Volatility Function, DVF |
| संबंधित≠ | 3 | 4 |
| सारांश≠ | 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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