(untitled) (gjstudies, 2021)
This paper introduces a novel, hybrid machine learning framework for climate-relatedfinancial stress testing (CRFST), addressing a critical gap in the integration of non-linearclimate dynamics with traditional financial risk models. Current approaches largely relyon historical data and linear projections, failing to capture the complex, cascading impactsof climate change on financial systems. Our methodology uniquely combines a QuantumInspired Neural Network (QINN) for modeling high-dimensional, non-linear relationshipsbetween climate variables and asset prices, with a Bio-Inspired Optimization Algorithm(BIOA) for stress scenario generation. The BIOA, modeled on adaptive immune systemresponse, evolves multi-hazard climate scenarios (e.g., concurrent heatwaves, floods, andpolicy shocks) that are physically plausible yet financially severe, moving beyond simplesensitivity analysis. We apply this framework to a synthetic dataset representing a diversifiedglobal portfolio, simulating financial stress under a range of climate pathways. Resultsdemonstrate that our model identifies critical, non-linear tipping points in asset correlationsand valuation shocks that are missed by conventional Value-at-Risk and scenario analysismodels. Specifically, we find that climate physical and transition risks interact in a superlinear fashion, amplifying losses by 40-60