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Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models
Antonis Papapantoleon / Serena Della Corte / Shaïn Afzali
machine learning / model calibrationResource overview

Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models

Paper2026

by Antonis Papapantoleon, Serena Della Corte, Shaïn Afzali

Tags
machine learningmodel calibrationneural networksrough volatilityvolatility surface

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Understand this paper

Suggested foundations for volatility models and surfaces and optimisation and numerical methods. Specialist prerequisites vary by paper.

Before you read

  1. Stochastic Processes, Brownian Motion and Itô's Lemma

    Follow diffusion-based models and their assumptions.

  2. ARCH and GARCH

    Separate conditional volatility from observed returns.

  3. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

  4. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

Go further

  1. Stochastic Volatility and the Heston Model

    Study a correlated stochastic-variance model.

  2. Reading the Implied Vol Surface — Moneyness, Term Structure, and Quoting Conventions

    Interpret prices across strikes and expiries.

  3. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

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Explore Advanced Derivatives and Exotic Options Explore Linear Algebra, Optimisation and Numerical Methods

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