Salīdzināt metodes

	Grieķi (Greeks) izmantojot automātisko diferenciāciju	Batesa modelis
Nozare	Kvantitatīvās finanses	Kvantitatīvās finanses
Saime	Machine learning	Regression model
Izcelsmes gads	2008	1996
Autors	Mike Giles, Iman Homescu	David S. Bates
Tips	Sensitivity Analysis	Equity/FX Model
Pirmavots	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.
Citi nosaukumi	AD Greeks, Algorithmic Differentiation, Autodiff	SVJ Model, Jump Diffusion
Saistītās	3	4
Kopsavilkums	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.