Portfolio Tail Risk Explorer
Adjust fire season severity, weather correlation, and portfolio size. Watch how correlated weather regimes amplify tail losses far beyond what independent models predict. The gap between the two curves is the risk your pricing model misses.
Average number of fires threatening the portfolio per season. Bad years average 10.
How much fire occurrence and severity share weather regimes. 0 = independent, 1 = fully correlated.
Number of WUI neighborhoods in the insurer's portfolio.
| Metric | Correlated | Independent | Ratio |
|---|---|---|---|
| Mean | -- | -- | -- |
| P90 | -- | -- | -- |
| P99 | -- | -- | -- |
The Correlation Premium
Start with the default settings and note the gap between the two curves. The correlated model (rose) and independent model (blue) agree on average losses — correlation barely changes the mean. But look at the right tail. At the 1-in-100-year level, correlated weather regimes produce losses far exceeding the independent assumption.
Now push weather correlation to 1.0. The curves diverge dramatically in the tail. This is the fundamental problem with pricing wildfire risk using independence assumptions: the expected loss looks fine, but the tail — the loss that bankrupts you — is wildly understated. Every neighborhood burns in the same bad year.
Try increasing portfolio size to 100 neighborhoods. Under independence, diversification works: the tail shrinks relative to the mean. Under correlation, diversification is an illusion. More neighborhoods just means more exposure to the same bad weather regime. The P99 ratio climbs.