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Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios
Jonathan Yu-Meng Li / Saeed Asadi
machine learning / optimizationResource overview

Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios

Paper2026

by Jonathan Yu-Meng Li, Saeed Asadi

Tags
machine learningoptimizationrisk management

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Suggested foundations for financial time series and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Stationarity and Unit Roots

    Check whether a relationship can be estimated consistently over time.

  2. Linear Regression

    Read the fitted relationship and its residuals.

  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.

Go further

  1. Cointegration and Error Correction

    Study long-run relationships without confusing them with correlation.

  2. Regression Assumptions and Diagnostics

    Check the assumptions behind reported estimates.

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

    Compare risk estimators and their assumptions.

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