방법 비교

	자동 미분(AD)을 이용한 그리스 계산	Bates 모형	무위험 중립 가치 평가
분야	금융공학	금융공학	금융공학
계열	Machine learning	Regression model	Regression model
기원 연도	2008	1996	1979
창시자	Mike Giles, Iman Homescu	David S. Bates	John Harrison and David Kreps
유형	Sensitivity Analysis	Equity/FX Model	Fundamental Principle
원전	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.	Harrison, J. M., & Kreps, D. M. (1979). Martingales and arbitrage in multiperiod securities markets. Journal of Economic Theory, 20(3), 381-408.
별칭	AD Greeks, Algorithmic Differentiation, Autodiff	SVJ Model, Jump Diffusion	Risk-Neutral Measure, Q-Measure
관련	3	4	4
요약	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.	Risk-neutral valuation (1979) is the fundamental principle that derivative prices equal the expected payoff discounted at the risk-free rate, computed under a risk-neutral probability measure (Q-measure). This principle, formalized by Harrison and Kreps, eliminates the need to estimate risk premia and is the foundation of modern derivatives pricing.