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Chaotic Bayesian Inference: Strange Attractors as Risk Models for Black Swan Events
Crystal Rust
bayesian statistics / risk managementResource overview

Chaotic Bayesian Inference: Strange Attractors as Risk Models for Black Swan Events

Paper2025

by Crystal Rust

Tags
bayesian statisticsrisk managementstatistical inferencesystemic risktail risk

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Understand this paper

Suggested foundations for probability and statistical reasoning and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  2. Conditional Expectation

    Interpret expectations after information is observed.

  3. Expected Shortfall and Other Tail Risk Measures

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

Go further

  1. Confidence Intervals and Hypothesis Testing

    Assess uncertainty rather than treating an estimate as a fact.

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

    Compare risk estimators and their assumptions.

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

    Check forecasts against realised losses.

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