পদ্ধতির তুলনা করুন

	স্বয়ংক্রিয় অন্তরীকরণ (AD) দ্বারা গ্রিকস (Greeks)	বেটস মডেল	ঝুঁকি-নিরপেক্ষ মূল্যায়ন
ক্ষেত্র	পরিমাণগত অর্থায়ন	পরিমাণগত অর্থায়ন	পরিমাণগত অর্থায়ন
পরিবার	Machine learning	Regression model	Regression model
উদ্ভবের বছর	2008	1996	1979
প্রবর্তক	Mike Giles, Iman Homescu	David S. Bates	John Harrison and David Kreps
ধরন	Sensitivity Analysis	Equity/FX Model	Fundamental Principle
মৌলিক উৎস	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.
অপর নাম	AD Greeks, Algorithmic Differentiation, Autodiff	SVJ Model, Jump Diffusion	Risk-Neutral Measure, Q-Measure
সম্পর্কিত	3	4	4
সারসংক্ষেপ	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.