grenier 4pm tues ven v.pdf

8/12/2019 GRENIER 4PM TUES VEN V.pdf

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Fill in the Blank:

y re a as rop e o e s so __________

C o m p l i c a t e d Wrong

Different

Expensive Cr i t ica l

Useful

RAA CAT Modeling 2014 2


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Discussion Topics

n ers an ng o e omponen s

Hazard: If models use the same data, how are they so different?

Engineering: How well do we understand building behavior?

Financial: Its just math. Is it that important?

Understanding Model Results

When can I trust the models?

Sanity checks on results

Dealing with uncertainty



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Catastrophe Model Components

HAZARD

EventGeneration ENGINEERING

Dama e

IntensityCalculation

Estimation

ExposureInformation LossCalculation

PolicyConditions



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US Hurricane

Where Do We Start? Historical Data

National Hurricane Centermaintains the HURDAT2database

Historical hurricane datafrom 1851 2012

Over 1700 individual events

ar qua e USGS develops National Seismic

Hazard Maps

Incorporates a wide array ofstudies to provide a scientificconsensus on earthquake hazard


,planning, etc.


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Simulate Parameters To Create a Catalog of Synthetic Events

n e parame ers es ma efrom historical data

Parameters used inana y ca w n emodel

Windfield propagatedover exposures to



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If the Sources are the Same, Why are Results Different?

HURDAT2 goes back to 1851, but is farfrom complete and accurate over theentire history Storms were missed and intensities

- reconnaissance (ca. 1944) and pre-satellite (ca. late 1960s) eras

Techniques have improved dramatically

Ship observations

primary data sourcesfor the first half of thehistorical record



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Early Years in the Historical Data are Missing Information

See Chris Landsea (NHC/NOAA) for an excellent summary:

http://www.aoml.noaa.gov/hrd/Landsea/gw_hurricanes/

RAA CAT Modeling 2014


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Coverage Gaps are Especially Evident in Eastern Atlantic

http://www.aoml.noaa.gov/hrd/Landsea/gw_hurricanes/

i i l

1933

i i lPicture comparing 2005 and 1933 seasons


2005


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Historical Events are Sparse in Time and Space

www.weather.com

On average, roughly 12 storms form inthe Atlantic Basin each year -

hurricanes (CAT 3-5) Fewer than 2 landfalls, with only 0.6

major landfalls per year


Earthobservatory.nasa.gov


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How Do the Modelers Handle the Data?

Is the risk lowerhere?

Smoothing techniques applied tohistorical data

and intensity Smoothing methodologies can be

different

Results in different risk profilesalong the coast


AIR Worldwide

model parameters


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Climate Conditioned Model Catalogs Add to Differences

AIR and RMS each have two different catalo s Standard/Long Term rates assume future activity follows the long termpattern captured in HURDAT2

WSST/Medium Term assumes we are currently in a period (since 1995) of

Significant Differences in Methodology:

AIR Separates historical record in warm years and cool years

Conditions their sampling methodology to consider only warm years ,

RMS Developed models to estimate sea surface temperatures and uses

multiple models to estimate landfall rates Developed regionalization model to distribute landfalls More complicated approach, but relies on a sequence of individual

models



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What is the Range of Landfall Frequencies?

Hurricanes (CAT1-2) and Major Hurricanes (CAT3-5) in 6 Regions Lines re resent difference in landfall rate com ared to historical

AIR and RMS long term and WSST/medium term views

WSST/Medium Term drive most (though not all)


-


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Sources of Uncertainty in Earthquake Models

n nown ear qua e au s 1994 M6.7 Northridge, CA earthquake previously unknown fault 9

miles below surface . ,

before Variability in deformation assumptions

Assumptions have significant impact on energy release

Uncertainty in earthquake rates and probabilities ow muc energy s s ore n a spec c reg on Which faults will trigger? How many? With what probability? 2011 M9.0 Tohoku Earthquake was stronger than previously though

Dealing with uncertainty leads to different assumptions, models andcatalogs



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HAZARD


Dama e


Estimation

Exposure

InformationLoss

Calculation

PolicyConditions



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Intensity Module Adjusts Windspeed for Local Conditions

Roughness Length Z 0 is a



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Loss Estimates are Very Sensitive to Roughness Assumptions

A few standard references exist However, no consensus view on estimates of roughness by land use

,

estimates by experienced practitioners can vary by a factor of 2 e.g. Simiu and Scanlan, (1996): Z 0 = 0.2 0.4 for suburbs

Assuming a standard log wind speed profile

Changing Z 0 from 0.2 to 0.3 for hurricane wind profilecan chan e surface winds b 5-7%

80 mph wind speed damage ratio = 2.4% 84 mph wind speed damage ratio = 3.3%

Assume 2000 structures, $500K per Loss estimates = $24M $33M



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HAZARD


Dama e


Estimation

Exposure

InformationLoss

Calculation

PolicyConditions



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Vulnerability Module Converts Intensity to Damage



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How Are Damage Functions Developed?

Claims Data Majority of claims are for residential policies

Mostly for recent events (Florida, Texas) where wind was a significantcause of loss

Each modeler interprets the results differently Published engineering studies

Post disaster surveys

Large scale model tests arebecoming more common



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Damageability Varies by Geography

AIR Building code zones (left)and RMS zones (below)

Includes modifiers for inland vs

Reflects differences in codesand code enforcement



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Variability Within and Across Occupancies Presents a Challenge

Office

Hotels

Retail



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Special Occupancies are Even More Challenging



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There is Significant Variability around the mean Damage Ratio

Similar buildings subjectedto same intensity can havedifferent amounts of loss

Modelers account for thisusin a distribution around the

mean damage ratio



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Financial Model is an Underappreciated Source of Differences

an o cons er uncer a n y n e mean amage ra o Secondary uncertainty to distinguish it from other sources of uncertainty Uncertainty in the loss, given an event

However, they use different assumptions:

AIR: uses customs r u on y per w

numerical convolution

RMS: assumes a betamean



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For Excess Policies, Small Differences in Shape can Have A LargeInfluence on Loss

Area between attachment (A)and limit (L)=

A L

a b i l i t y

P r o

.

Loss



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Detailed, Accurate Data is Useful

Address Geocode Building Contents Time Limits DeductiblesReplacement ValuesLocation Policy Terms

Construction Occupancy Age Height Shutters Roof Shape Foundation Type

Building Attributes Building Details

significant simplification

of reality



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Generally, Results are Less Reliable for Small, Complex Exposures

NationwideHomeowners QS

Floridaomeowners

QS

l i o s i z e

Commercial

CountrywideCommercial

Excess i n g p o r t

f

D e c r e a

Complex Commercial XPR


Increasing portfolio complexity


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Interpreting the Exceedance Probability Curve

EV 4575 641

10 841 e r t a

i n t y As a general rule, uncertaintyincreases as the number of

events decreases25 1,093

50 1,315100 1,554

a s

i n g u n

With increasing geographicdetail,

500 2,3921000 3,090 I

n c r


Increasing uncertainty


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Sanity Checks

s or ca esu s Modelers provide historical event catalogs to allow as-if analyses Understand exposure change to interpret results + - reasona ty t res o ; oo or as

Market share of loss Compute market share of industry loss (vs exposure/premium) Aggregate models are useful for this purpose

Old-fashioned aggregates

Useful on their own, and are a good benchmark for model assessment PML/Limit ratios, regional and historical trends become good methods

for tracking results over time



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Industry Trend: Deeper Analytics, Greater Transparency



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