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Efficient Monte Carlo Valuation of Corporate Bonds in Financial Networks
Agostino Capponi / Dohyun Ahn
credit risk / financial networksResource overview

Efficient Monte Carlo Valuation of Corporate Bonds in Financial Networks

Paper2026

by Agostino Capponi, Dohyun Ahn

Tags
credit riskfinancial networksfixed incomemonte carlo simulationnumerical methodssystemic risk

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Suggested foundations for monte carlo and numerical simulation and risk measurement and stress. Specialist prerequisites vary by paper.

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  1. Laws of Large Numbers and the Central Limit Theorem

    Understand convergence, sampling error and confidence intervals.

  2. Random Number Generation

    Generate reproducible samples and distinguish randomness from model assumptions.

  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.

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  1. Monte Carlo Option Pricing

    Turn a discounted payoff into an estimator.

  2. Correlated Randoms and Low-Discrepancy Sequences

    Study dependence and alternatives to independent sampling.

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

    Compare risk estimators and their assumptions.

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