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Primary and Secondary Uncertainty
 Primary Uncertainty asks the question of “will a particular scenario happen?”
It is expressed in the event loss table exceedance rates. 3% chance of an event
happening  97% chance it doesn’t.
Secondary Uncertainty asks the question of “When a scenario happens, what is
the range of specific results?” This is reflected in event-by-event confidence
ranges. Is a 7.2 on the Hayward fault in downtown San Jose a $10Bn. event
or a $25Bn. event?
Total Uncertainty is a blend of both primary and secondary uncertainty,
reflecting that some items diversify across possible scenarios (what is the level
of tide at landfall?) and some do not (are building codes really enforced?).
Non-Diversifiable Uncertainty
Common Sources of Model Uncertainty
Event size, location and timing
Actual structural vulnerability
Varying construction codes
Changes in force levels as events progress
Local surface variations and measurement uncertainty
Missiles damage (related to soil condition, foliage, prior damage)
Missing or Non-Modeled exposure
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LAE |
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Power Outages |
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Demand Surge |
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Incomplete Data (e.g., Autos, Scheduled risks) |
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Flood |
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Incorrect Valuations (e.g., ITV) |
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Tornadoes |
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Missile Damage
Models Are Very Good at Considering:
Coverages A and B
Experimental data from wind tunnel/shake table tests
Number of major historical events for large geographic regions
(e.g. a state or fault)
Reconciliation to industry data, such as PCS or Sigma
25-year to 75-year return times
Structure Loss vs. Construction Data
Two Houses, Two Fates
Two houses on Belmont Street
in the Old Est Hills Historic
District of Pensacola had
distinctly different fates when
Ivan hit. The house on the
right was built in 1903 and
refurbished. The house on the
left was built by Habitat for
Humanity and is only a few
years old.
Although Less Data is Available, Modelers Use Reasonable
Formulas
to Handle Other Characteristics:
Coverage C and Automobiles, WC and Life
Insurance-to-Value factors
Deductibles
Inuring Coverages
Construction and Occupancy
Demand Surge
Peak gusts and multiple shake frequencies
The stringency of building codes, by year built and jurisdiction
Long-term changes in the patterns of reporting data observations
Finer scale (county, town) topography
Fire following, terrorism
Automobile Loss Hazard
Cool Truck
A truck ended up in
the swimming pool in
back of a home in
Pensacola’s Grande
Lagoon area, where
Ivan’s powerful winds
and storm surge took
their greatest toll.
Peak Gust Variation
Twister
A Waterspout
forms off of
Fort Myers on
Aug. 12, the
day before
Hurricane
Charley
struck
Florida’s west
coast.
More Subtle Factors that the Models Can Only
Implicitly Reflect:
Coverage D and Risk Excess layers
Secondary uncertainty/ Correlation issues
The degree of enforcement of local building codes
Foliage
Weather patterns before and after loss events
Physical alignment of structures along events’ force vectors
Local variations of concentrations or hazard (street address detail)
Changes in claims handling and other industry practices
Also:
Data for medium-sized events, 0-10 year return time losses, are not collected as consistently as
for larger events. Modelers must look to larger events and back into these events
Data for mega-events, 250+ year return time losses, are also missing due to limited history.
Modelers must extrapolate loss potentials from smaller events
Demand Surge (for blue tarps)
“So, Why Do We Use Models At All?”
Models provide highly-educated guesses.
- Reduce the parameters of the unknown.
- Model differences are unsettling, but help calibrate the degree of uncertainty.
- A starting point, but certainly not an ending point
Models leverage a ton of (continually-evolving) scientific research and engineering work.
Models enable a more consistent and dynamic approach to underwriting and
risk-management.
Outside stakeholders require it as a means of justification.
- Rating agencies
- Regulators
- Reinsurers
- Analysts
Parameters of the Unknown
Wooded Lot
This two-by-four plank was apparently driven by Hurricane
Jeanne’s 100-mph winds
into the blacktop of the parking lost
at Avalon State Park on North Hutchinson Island
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