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On deep calibration of (rough) stochastic volatility models
Aitor Muguruza / Benjamin Stemper / Blanka Horvath / Christian Bayer / Mehdi Tomas
deep learning / machine learningResource overview

On deep calibration of (rough) stochastic volatility models

Paper2019

by Aitor Muguruza, Benjamin Stemper, Blanka Horvath, Christian Bayer, Mehdi Tomas

Tags
deep learningmachine learningmodel calibrationneural networksoption pricingrough volatility

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Understand this paper

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

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