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Robust Bernoulli Mixture Models for Credit Portfolio Risk
Eva Lütkebohmert / Jonathan Ansari
credit risk / risk managementResource overview

Robust Bernoulli Mixture Models for Credit Portfolio Risk

Paper2024

by Eva Lütkebohmert, Jonathan Ansari

Tags
credit riskrisk management

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Understand this paper

Suggested foundations for risk measurement and stress and credit and counterparty risk. 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. Credit Instruments, PD/LGD/EAD, and Rating Migration

    Separate default probability, loss severity and exposure.

  4. Conditional Expectation

    Read conditional default and exposure quantities.

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. Threshold Models and the Gaussian Copula for Portfolio Default

    Study dependence between defaults.

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Lesson links open public previews. Access to full lessons depends on the topic and your membership.

Explore Quantitative Risk Management Explore Counterparty Credit Risk and CVA

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