How do banks conduct climate scenario analysis for physical risk, and what methodologies translate climate events into financial losses?
I'm studying FRM Part II emerging risk topics and climate risk scenario analysis keeps coming up. I understand that physical risk refers to actual climate events (flooding, heat waves, wildfires), but I'm unsure how banks quantify the impact on their loan portfolios. What frameworks exist, and what data challenges arise?
Climate scenario analysis for physical risk assesses how acute events (hurricanes, floods, wildfires) and chronic changes (sea-level rise, temperature increase) affect a bank's asset values, loan performance, and capital adequacy. Unlike traditional stress testing, climate scenarios extend over decades and require geospatial data integration.
Methodological Framework:
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Scenario Pathways (NGFS):
| Scenario | Temperature by 2100 | Physical Risk Level |
|---|---|---|
| Net Zero 2050 | +1.5C | Lower physical, higher transition |
| Below 2C | +1.7C | Moderate both |
| Current Policies | +3.0C+ | Highest physical risk |
| Delayed Transition | +1.8C | High transition, moderate physical |
Worked Example: Peninsula Savings Bank has a 4.2 billion residential mortgage portfolio concentrated in coastal areas. Under a \"Current Policies\" scenario:\n\nStep 1 -- Hazard assessment:\n- 12% of collateral properties (504M) are in FEMA 100-year flood zones
- Climate models project 100-year flood frequency increasing to 1-in-30-year events by 2050
Step 2 -- Damage estimation:
- Expected flood damage for affected properties: 28% of property value (based on depth-damage functions)
- Projected losses on collateral: 141.1 million**
Step 3 -- Credit impact:
- LTV increases from 72% (origination) to 100%+ for flooded properties
- Projected PD increase: 2.1% baseline to 8.7% for affected segment (underwater borrowers)
- LGD increases from 25% to 52% (damaged collateral)
Step 4 -- Portfolio loss:
- Expected loss on affected segment: 22.8 million**
- Unaffected portfolio: 19.4 million**
- Total expected loss: **26.5M baseline = 59% increase)
Data Challenges:
- Property-level geolocation requires matching loan records to precise coordinates
- Damage functions vary by construction type, elevation, flood depth, and duration
- Insurance coverage reduces bank losses but may become unavailable in high-risk zones
- Non-linear tipping points (ice sheet collapse, ecosystem failure) are poorly modeled
- Horizon mismatch: climate scenarios span 30-80 years while bank risk horizons are 1-5 years
Regulatory Expectations: Supervisors (ECB, Bank of England, Fed) increasingly require banks to demonstrate climate scenario capability, even if results are not yet tied to Pillar 1 capital. The focus is on governance, methodology, and strategic integration.
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