Usporedite metode

	Računanje grčkih slova (Greeks) pomoću automatske diferencijacije	Batesov model
Područje	Kvantitativne financije	Kvantitativne financije
Obitelj	Machine learning	Regression model
Godina nastanka	2008	1996
Tvorac	Mike Giles, Iman Homescu	David S. Bates
Vrsta	Sensitivity Analysis	Equity/FX Model
Temeljni izvor	Giles, M. B. (2008). Adjoint code by automatic differentiation. Journal of Computational Finance, 12(1), 1-18.	Bates, D. S. (1996). Jumps and stochastic volatility: Exchange rate processes implicit in Deutsche Mark options. Review of Financial Studies, 9(1), 69-107.
Drugi nazivi	AD Greeks, Algorithmic Differentiation, Autodiff	SVJ Model, Jump Diffusion
Srodne	3	4
Sažetak	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.