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Nonparametric calibration of jump-diffusion option pricing models.
Peter Tankov / Rama Cont
jump processes / model calibrationResource overview

Nonparametric calibration of jump-diffusion option pricing models.

Paper2004

by Peter Tankov, Rama Cont

Tags
jump processesmodel calibrationnumerical methodsoptimizationoption pricing

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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.

Go further

  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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Lesson links open public previews. Access to full lessons depends on the topic and your membership.

Explore Linear Algebra, Optimisation and Numerical Methods Explore Derivatives Pricing and Computational Finance

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