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On the Estimation of Own Funds for Life Insurers: A Study of Direct, Indirect, and Control Variate Methods in a Risk-Neutral Pricing Framework
Mark-Oliver Wolf
insurance / monte carlo simulationResource overview

On the Estimation of Own Funds for Life Insurers: A Study of Direct, Indirect, and Control Variate Methods in a Risk-Neutral Pricing Framework

Paper2025

by Mark-Oliver Wolf

Tags
insurancemonte carlo simulationnumerical methods

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Suggested foundations for risk measurement and stress and monte carlo and numerical simulation. 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. Laws of Large Numbers and the Central Limit Theorem

    Understand convergence, sampling error and confidence intervals.

  4. Random Number Generation

    Generate reproducible samples and distinguish randomness from model assumptions.

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  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. Monte Carlo Option Pricing

    Turn a discounted payoff into an estimator.

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