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A Space Mapping approach for the calibration of financial models with the application to the Heston model
Anna Clevenhaus / Claudia Totzeck / Matthias Ehrhardt
model calibration / numerical methodsResource overview

A Space Mapping approach for the calibration of financial models with the application to the Heston model

Paper2025

by Anna Clevenhaus, Claudia Totzeck, Matthias Ehrhardt

Tags
model calibrationnumerical methodsoptimizationoption pricingpartial differential equationsstochastic differential equations

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Suggested foundations for optimisation and numerical methods and volatility models and surfaces. 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. Stochastic Processes, Brownian Motion and Itô's Lemma

    Follow diffusion-based models and their assumptions.

  4. ARCH and GARCH

    Separate conditional volatility from observed returns.

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. Stochastic Volatility and the Heston Model

    Study a correlated stochastic-variance model.

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