Bandingkan kaedah

	Yunani melalui Pembezaan Automatik	Volatiliti Lokal (Dupire)	Penilaian Bebas Risiko
Bidang	Kewangan Kuantitatif	Kewangan Kuantitatif	Kewangan Kuantitatif
Keluarga	Machine learning	Regression model	Regression model
Tahun asal	2008	1994	1979
Pengasas	Mike Giles, Iman Homescu	Bruno Dupire	John Harrison and David Kreps
Jenis	Sensitivity Analysis	Equity/FX Model	Fundamental Principle
Sumber perintis	Giles, M. B. (2008). Adjoint code by automatic differentiation. Journal of Computational Finance, 12(1), 1-18.	Dupire, B. (1994). Pricing with a smile. Risk Magazine, 7(1), 18-20.	Harrison, J. M., & Kreps, D. M. (1979). Martingales and arbitrage in multiperiod securities markets. Journal of Economic Theory, 20(3), 381-408.
Alias	AD Greeks, Algorithmic Differentiation, Autodiff	Deterministic Volatility Function, DVF	Risk-Neutral Measure, Q-Measure
Berkaitan	3	4	4
Ringkasan	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.	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.	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.