GRA · Climate and Nature Financial Risk
Spatial financeWhere capital meets hazard, exposure and transition
Insurance bank investment effects
Climate and nature risks reach insurers, banks and investors through different channels and interact between them. Insurers face higher claims and may withdraw cover; banks then hold uninsured collateral; investors reprice insurers, banks and real assets together. This page sets out how effects differ by sector and feed between them, how they are measured, and who decides.
Insurance bank investment effects on the record.
Insurance bank investment effects: how the risk develops.
Distinct financial-sector transmission. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Insurers reprice first
Insurers renew annually and can raise prices or exit quickly. Their decisions change the risk carried by property owners and lenders.
Banks hold longer exposures
Mortgages and loans last decades. When insurance becomes unavailable, banks bear physical risk on collateral they cannot easily exit.
Investors price expectations
Equity and bond prices respond to expected costs and policy. Sudden repricing can affect insurers' and banks' investment portfolios too.
Feedback across sectors
Insurance withdrawal lowers property values, raising bank losses and reducing investor returns. Sector-by-sector analysis misses this loop.
Exposure, hazard and the exceedance curve.
Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Insurance non-renewals in high-risk areas | count | annual | Regulator data | Published statistics |
| Mortgages on property without hazard insurance | share of mortgage book | periodic | Bank and supervisory data | REPORTED |
| Insurer investment exposure to carbon-intensive sectors | currency and share | annual | Insurer disclosures | DECLARED |
| Bank exposure to insurers and insured collateral | currency | annual | Supervisory data | Published statistics |
| Climate-related losses in investment portfolios under scenarios | percent | per exercise | Scenario analysis | MODELLED |
| Market pricing of climate risk | spread or return difference | periodic | Academic and central bank studies | ESTIMATED |
Nature risk screening.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Nature risk screening
Insurance bank investment effects · Nature-sensitive sectors · LEAP cycle 2
LEAP
- Locateborrower sites
- Evaluatedependencies · impacts
- Assesscredit and reputational risk
- Preparetargets · disclosure
Dependencies by sector
| Agriculture | 3 | High | High | High | High |
| Food processing | 2 | High | Medium | Medium | Medium |
| Mining | 2 | High | Low | Medium | Medium |
| Real estate | 6 | Medium | Low | Low | Low |
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| EIOPA climate stress test reports | Insurer exposure to climate risk | Free, EU reuse terms |
| NAIC climate risk disclosure survey | Insurer climate risk governance and strategy | Free, NAIC terms |
| Federal Insurance Office homeowners insurance data | Premiums, non-renewals and claims by area | Public domain |
| IMF Global Financial Stability Report climate chapters | Cross-sector analysis of climate financial risk | Free, IMF terms |
| NGFS reports on cross-sector transmission | Analysis of interaction between insurance and banking | Free, NGFS terms |
How a shock to assets becomes a system loss.
The equity the system loses after interbank contagion, against the initial hit, as leverage, interconnection and recovery rates change, the core of a macroprudential stress test. Teaching model with stated illustrative values, not a prediction. The map on this page shows Southeast Asia; the model itself is not tied to a place.
Controls
- Fall in external assets
- 2.0%
- Leverage (assets over equity)
- 10 times
- Interbank share of assets
- 10%
- Recovery on interbank claims
- 40%
- Banks in the system
- 20
- Core banks
- 20% of banks
Readouts
- Equity lost to the initial shock
- 18.0%
- Equity lost after contagion
- 45.0%
- Added by contagion (DebtRank)
- 27.0 points
- Banks in default
- 0
Numbers, methods and sources
| Round | System equity lost (%) | Banks in default |
|---|---|---|
| 0 | 18.00 | 0 |
| 1 | 28.80 | 0 |
| 2 | 35.28 | 0 |
| 3 | 39.17 | 0 |
| 4 | 41.50 | 0 |
| 5 | 42.90 | 0 |
| 6 | 43.74 | 0 |
| 7 | 44.24 | 0 |
| 8 | 44.55 | 0 |
| 9 | 44.73 | 0 |
| 10 | 44.84 | 0 |
| 11 | 44.90 | 0 |
| 12 | 44.94 | 0 |
| 13 | 44.96 | 0 |
| 14 | 44.98 | 0 |
| 15 | 44.99 | 0 |
| 16 | 44.99 | 0 |
| 17 | 45.00 | 0 |
| 18 | 45.00 | 0 |
| 19 | 45.00 | 0 |
| 20 | 45.00 | 0 |
| 21 | 45.00 | 0 |
| 22 | 45.00 | 0 |
| 23 | 45.00 | 0 |
| 24 | 45.00 | 0 |
| 25 | 45.00 | 0 |
| 26 | 45.00 | 0 |
| 27 | 45.00 | 0 |
| 28 | 45.00 | 0 |
| 29 | 45.00 | 0 |
| 30 | 45.00 | 0 |
Methods
- DebtRank Battiston and others (2012), Scientific Reports 2:541
- Differential DebtRank Bardoscia and others (2015), PLOS ONE 10
- Contagion in financial networks Gai and Kapadia (2010), Proc. R. Soc. A 466
- Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Climate and Nature Financial Risk in Foundry
- Tested at Climate and Nature Financial Risk in Universe
- Codified in Climate and Earth Systems in Standards
- Published in Climate and Nature Financial Risk in Reports
- Taught in Faculty of Earth and Environmental Sciences in Academy
Who decides, and what the evidence serves.
Climate and Nature Financial Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Insurance and banking supervisors | Whether to coordinate monitoring of insurance withdrawal and mortgage risk | Cross-sector data and scenario evidence |
| Bank risk functions | How insurance availability affects collateral risk | Insurance data linked to loan books |
| Insurer boards | How underwriting and investment climate exposures interact | Asset and liability scenario analysis |
| Governments and public pools | Whether to intervene in insurance markets | Availability, affordability and cross-sector evidence |
Outputs this topic produces
- Guide to climate transmission by financial sector
- Brief on the insurance-banking feedback loop
- Method note on cross-sector scenario analysis
- Questions for supervisors on joint monitoring
Trace a shock in insurance bank investment effects to the balance sheet.
Assembled from the Nexus Standards area profile Central banks and financial supervisors: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Tropical cyclone
- Earthquake
- Ice-jam flood
- Wildfire
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Aftershock sequence (set off downstream)
- Fault surface rupture (set off downstream)
- Soil liquefaction (set off downstream)
- Slope slide (set off downstream)
- Rockfall (set off downstream)
- Tsunami (set off downstream)
Systems and couplings
- Central banks and financial supervisors
- Banking Coupling
- Insurance and reinsurance Coupling
- Asset owners and asset management Coupling
- Financial and economic risk standards Coupling
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-C5Scenarios and stress testingNRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-H118Hazard family: Organised crime and illicit economies (NHS-1 soc.crime)
Observation
- IMF data services (IFS, FSI, WEO, GFS) (listed for reference)
- BIS statistics (banking, credit, debt securities, property prices) (listed for reference)
- ECB Data Portal (listed for reference)
- NGFS Scenario Explorer (listed for reference)
- Bank nonperforming loans to total gross loans (%)
- Bank capital to assets ratio (%)
- Macroeconomic Dashboard
Example geography
- Western Europe: 152 hexagonal cells
- Rain rate, last half hour (IMERG), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Carbon monoxide, smoke transport (AIRS), NASA GIBS
What sector specialists ask.
- How do short insurance contract terms compare with long loan and investment horizons?
- How quickly does physical risk show up in claims, defaults and prices?
- How do insurers' pricing and capacity decisions affect banks and investors?
- Where could correlated responses across sectors amplify stress?
What insurance bank investment effects means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Scenario pathways
How insurance bank investment effects could be stressed under the scenario families central banks and supervisors use.
| Scenario family | Transition risk | Physical risk | What a stress test asks |
|---|---|---|---|
| Orderly | Early, gradual policy | Lower | Can the book absorb a steady repricing? |
| Disorderly | Late, abrupt policy | Lower to medium | What breaks under a sudden repricing and credit migration? |
| Hot house world | Limited policy | High and rising | Where do physical losses concentrate in collateral and insurance? |
Distinct transmission channels.
Property and casualty insurers usually write annual contracts, so they can reprice or withdraw quickly as risk rises; their exposure is to the frequency and severity of claims. Banks hold multi-year loans secured on property and business assets, so falling collateral values and borrower stress accumulate over time. Investors hold securities that reprice as markets reassess risk, sometimes suddenly.
The sectors interact. If insurers withdraw from high-risk areas, uninsured properties may lose value, raising credit risk for banks. Life insurers and pension funds, as long-term investors, face transition risk on the asset side of their balance sheets.
Sector-specific analysis.
- 01
Insurance
Catastrophe modelling and pricing analysis, with climate and atmospheric science. Models need adjustment for climate change.
- 02
Banking
Credit risk models incorporating climate effects on probability of default and collateral, using financial economics and macro-finance. Long-horizon effects are hard to fit into existing models.
- 03
Investment
Portfolio repricing and exposure analysis. Market reactions are hard to predict.
- 04
Cross-sector analysis
Studying interactions, with scenario design and stress testing. Interaction effects are rarely modelled.
How it is studied, and what the methods cannot say.
- 01Financial economics and macro-financeCredit risk transmission · Market pricing studies · Cross-sector transmissionApplied here
- 02Scenario design and stress testingTranslating NGFS scenarios · Short-term climate scenarios · Dynamic balance sheetsApplied here
- 03Spatial statistics and geostatisticsGeolocating exposures · Aggregating location risk · Resolution sensitivityApplied here
- 04Climate and atmospheric scienceHazard projections for portfolios · Tail and compound hazards · Scenario physical variablesApplied here
Questions and answers.
Why do insurance decisions matter for banks?
Do markets price climate risk?
What is a cross-sector feedback loop?
Why do insurers feel climate risk first?
How does insurance affect banks?
Does the platform assess individual institutions?
Are pension funds exposed?
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