Introduction

Climate risk value-at-risk (CVaR)—potential portfolio loss from climate scenarios—requires realistic scenario generation. Machine learning generative models (VAE, GAN) trained on historical climate and economic data generate realistic scenarios combining temperature paths, policy responses, and economic impacts. CVaR calculated from generated scenarios more accurately reflects true climate risk than ad-hoc scenarios.

ML Scenario Generation

Combine climate model outputs (IPCC projections) with economic response models. Train generative models to produce internally consistent scenarios: given temperature path, generate plausible policy response, economic growth impact, and asset price impacts. Generate thousands of scenarios; estimate CVaR (5th percentile portfolio loss) across all scenarios.

Application

Compute CVaR for climate scenarios. If climate CVaR is concentrated in climate-vulnerable assets, increase hedges or reduce exposure. If well-diversified, risk is manageable. Use CVaR for portfolio-level climate risk budgeting.

Conclusion

ML-generated climate scenarios enable sophisticated climate risk quantification via CVaR, improving portfolio resilience.