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| Greki za pomocą automatycznego różniczkowania× | Model Batesa× | |
|---|---|---|
| Dziedzina | Finanse ilościowe | Finanse ilościowe |
| Rodzina≠ | Machine learning | Regression model |
| Rok powstania≠ | 2008 | 1996 |
| Twórca≠ | Mike Giles, Iman Homescu | David S. Bates |
| Typ≠ | Sensitivity Analysis | Equity/FX Model |
| Źródło pierwotne≠ | Giles, M. B. (2008). Adjoint code by automatic differentiation. Journal of Computational Finance, 12(1), 1-18. link ↗ | Bates, D. S. (1996). Jumps and stochastic volatility: Exchange rate processes implicit in Deutsche Mark options. Review of Financial Studies, 9(1), 69-107. DOI ↗ |
| Inne nazwy≠ | AD Greeks, Algorithmic Differentiation, Autodiff | SVJ Model, Jump Diffusion |
| Pokrewne≠ | 3 | 4 |
| Podsumowanie≠ | 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. | The Bates model (1996) combines stochastic volatility and jump diffusion to capture both the volatility smile and the implied volatility skew observed in equity and currency option markets. It extends the Heston model by adding a Poisson jump component to returns, making it suitable for pricing options when sudden price moves are expected. |
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