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روش کرانک-نیکلسون×مدل هال-وایت×مدل نوسانات محلی (Dupire)×
حوزهمالی کمّیمالی کمّیمالی کمّی
خانوادهMachine learningRegression modelRegression model
سال پیدایش194719901994
پدیدآورJohn Crank and Phyllis NicolsonJohn C. Hull and Alan WhiteBruno Dupire
نوعPDE SolverInterest Rate ModelEquity/FX Model
منبع بنیادینCrank, J., & Nicolson, P. (1947). A practical method for numerical evaluation of solutions of partial differential equations of the heat-conduction type. Mathematical Proceedings of the Cambridge Philosophical Society, 43(1), 50-67. DOI ↗Hull, J., & White, A. (1990). Pricing interest-rate-derivative securities. Review of Financial Studies, 3(4), 573-592. DOI ↗Dupire, B. (1994). Pricing with a smile. Risk Magazine, 7(1), 18-20. link ↗
نام‌های دیگرCN Method, Implicit Finite DifferenceExtended Vasicek, Generalized VasicekDeterministic Volatility Function, DVF
مرتبط344
خلاصهThe Crank-Nicolson method is a widely-used implicit finite difference scheme for solving PDEs in option pricing. It provides second-order accuracy in both space and time, unconditional stability, and can efficiently price derivatives with early exercise features (American options) or complex boundary conditions.The Hull-White model (1990) is a one-factor short-rate model with time-dependent mean reversion and volatility, designed to fit the initial yield curve exactly. It generalizes the Vasicek model to allow better calibration to observed bond and derivative prices, and is widely used for pricing interest rate exotics and managing interest rate risk.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.
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ScholarGateمقایسهٔ روش‌ها: Crank-Nicolson Pricing · Hull-White Model · Local Volatility (Dupire). بازیابی‌شده در 2026-06-19 از https://scholargate.app/fa/compare