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Adaptive calibration of Heston Model using PCRLB based switching Filter
Kumar Yashaswi
bayesian statistics / model calibrationResource overview

Adaptive calibration of Heston Model using PCRLB based switching Filter

Paper2021

by Kumar Yashaswi

Tags
bayesian statisticsmodel calibrationstatistical inferencestochastic processes

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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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