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Paper / 2018SMQ / 242938302951736
Diffusion equations: convergence of the functional scheme derived from the Binomial tree with local volatility for non smooth payoff functions.
Emmanuel Lépinette / Julien Baptiste
numerical methods / partial differential equationsResource overview

Diffusion equations: convergence of the functional scheme derived from the Binomial tree with local volatility for non smooth payoff functions.

Paper2018

by Emmanuel Lépinette, Julien Baptiste

Tags
numerical methodspartial differential equations

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

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  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.

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  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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