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Paper / 2025SMQ / 180620313115785
Combination of traditional and parametric insurance: calibration method based on the optimization of a criterion adapted to heavy tail losses
Daniel Nkameni / Olivier Lopez
insurance / optimizationResource overview

Combination of traditional and parametric insurance: calibration method based on the optimization of a criterion adapted to heavy tail losses

Paper2025

by Daniel Nkameni, Olivier Lopez

Tags
insuranceoptimizationtail risk

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Understand this paper

Suggested foundations for optimisation and numerical methods and risk measurement and stress. 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. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

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. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

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