SOURCE-LINKED INTELLIGENCE

Linking Climate Extremes to Financial Risk in the Era of Climate Change

CORDIS · observation · Publication date unknown

OCKCLIM addresses this challenge by developing the first integrated framework to detect, project, and quantify the financial impacts of extreme climate events. The project combines recent advances in machine learning with econometric modeling to map the entire pathway from hazard to market response. Capsule Networks will be trained on reanalysis (ERA5), climate simulations (CESM2-LENS2), and disaster records (EM-DAT) to detect heatwaves, floods, and tropical cyclones. The trained models will then be applied to future climate scenarios (SSP3-7.0) to project shifts in event frequency and intensity. Detected events will be aligned with financial datasets, including stock indices, sector-level returns, and volatility measures. Econometric tools, such as event studies, VAR, GARCH, and GARCH-MIDAS, will be used to estimate short- and medium-term market impacts and develop scenario-based climate stress tests for key financial sectors. Expected outputs include open-source machine learning models, catalogs of historical and projected extremes, high-impact publications, and a lightweight visua

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recordType

award

status

SIGNED

region

EU

value

194074.56

unit

EUR