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An Adaptive and Explicit Fourth Order Runge-Kutta-Fehlberg Method Coupled with Compact Finite Differencing for Pricing American Put Options
Chinonso Nwankwo / Weizhong Dai
numerical methods / option pricingResource overview

An Adaptive and Explicit Fourth Order Runge-Kutta-Fehlberg Method Coupled with Compact Finite Differencing for Pricing American Put Options

Paper2020

by Chinonso Nwankwo, Weizhong Dai

Tags
numerical methodsoption pricingpartial differential equations

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Suggested foundations for optimisation and numerical methods and options and derivatives pricing. Specialist prerequisites vary by paper.

Before you read

  1. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

  2. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

  3. The Binomial Model

    Build replication and risk-neutral probabilities in a tractable model.

  4. Market Model and Risk-Neutral Valuation

    Distinguish pricing probabilities from physical forecasts.

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  1. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

  2. Numerical Conditioning and Stability

    Separate model error from unstable numerical computation.

  3. The Black-Scholes PDE, Formula and Implied Volatility

    Connect the pricing equation, closed-form value and implied volatility.

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