Salīdzināt metodes

	Grieķi (Greeks) izmantojot automātisko diferenciāciju	Batesa modelis	Vietējā volatilitāte (Dupire)
Nozare	Kvantitatīvās finanses	Kvantitatīvās finanses	Kvantitatīvās finanses
Saime	Machine learning	Regression model	Regression model
Izcelsmes gads	2008	1996	1994
Autors	Mike Giles, Iman Homescu	David S. Bates	Bruno Dupire
Tips	Sensitivity Analysis	Equity/FX Model	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.	Dupire, B. (1994). Pricing with a smile. Risk Magazine, 7(1), 18-20.
Citi nosaukumi	AD Greeks, Algorithmic Differentiation, Autodiff	SVJ Model, Jump Diffusion	Deterministic Volatility Function, DVF
Saistītās	3	4	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.	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.