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Nash Peer-to-Peer Insurance Bargaining under Price Fairness and Coalitional Stability
Kenneth Tsz Hin Ng / Tak Wa Ng / Tim J. Boonen / Wing Fung Chong
insurance / optimizationResource overview

Nash Peer-to-Peer Insurance Bargaining under Price Fairness and Coalitional Stability

Paper2026

by Kenneth Tsz Hin Ng, Tak Wa Ng, Tim J. Boonen, Wing Fung Chong

Tags
insuranceoptimization

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Suggested foundations for risk measurement and stress and optimisation and numerical methods. Specialist prerequisites vary by paper.

Before you read

  1. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  2. Expected Shortfall and Other Tail Risk Measures

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

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

    Compare risk estimators and their assumptions.

  2. Backtesting VaR and ES: Coverage Tests and Model Validation

    Check forecasts against realised losses.

  3. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

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