Adaptation Using Financial Markets: Climate Risk Diversification through Securitization
Bibliographic record
Abstract
In the face of rising climate risk, financial institutions may adapt by transferring such risk to securitizers that have the skill and expertise to build diversified pools, such as Mortgage-Backed Securities.In diversified pools, exposure to climate risk may be a drop in the ocean of cash flows.This paper builds a data set of the entire securitization chain from mortgage-level to MBS deallevel cash flows, and observes the prices of the tranches at monthly frequency.Wildfires lead to higher rates of prepayment and foreclosure at the mortgage level, and larger losses during foreclosure sales.At the MBS deal level, a lower spatial concentration of dollar balances (lower spatial dollar Herfindahl), a lower spatial correlation in wildfire events (within-deal correlation), leads to a lower exposure to wildfire events.These quantifiable metrics of diversification identify those existing deals whose design makes them resilient to climate change.This paper builds optimal deals by finding the portfolio weights in an asset demand system that targets return and risk.Extrapolating wildfire risk using a granular wildfire probability model and temperature projections in 2050, we build climate resilient MBSs whose returns are minimally impacted by wildfire risk even as they supply mortgage credit to wildfire prone areas.Finally, we test whether the market prices the sensitivity of each deal's cash flow to wildfire risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".