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Time-consistent catastrophe risk management under the path-dependent effects
Liyuan Cui / Wenyuan Li
insurance / optimizationResource overview

Time-consistent catastrophe risk management under the path-dependent effects

Paper2025

by Liyuan Cui, Wenyuan Li

Tags
insuranceoptimizationrisk managementrough volatilitystochastic calculusstochastic processes

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Understand this paper

Suggested foundations for risk measurement and stress and stochastic processes and pricing. 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. Conditional Expectation

    Understand what it means to condition on current information.

  4. Stochastic Processes, Brownian Motion and Itô's Lemma

    Follow Brownian dynamics and the second-order term in Itô calculus.

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. Market Model and Risk-Neutral Valuation

    Connect stochastic dynamics to no-arbitrage prices.

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