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Decomposition formula for rough Volterra stochastic volatility models
Jan Pospíšil / Josep Vives / Raul Merino / Tommi Sottinen / Tomáš Sobotka
model calibration / monte carlo simulationResource overview

Decomposition formula for rough Volterra stochastic volatility models

Paper2019

by Jan Pospíšil, Josep Vives, Raul Merino, Tommi Sottinen, Tomáš Sobotka

Tags
model calibrationmonte carlo simulationnumerical methodsoption pricingrough volatilitystochastic processes

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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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