modeling risks of earthquake and hurricane in …...the new eqecat creating value through increased...

The New EQECAT Creating Value Through Increased Transparency

Modeling Risks of Earthquake Modeling Risks of Earthquake and Hurricane in Mexico

Dennis E. KuzakSenior Vice PresidentSenior Vice President

25 Mayo 2010

Creating Value Through Increased TransparencyProprietary and Confidential

Agenda

Overview of Mexico hurricane and earthquake RisksModeling mechanicsThe future challenges in risk modeling


Modeling MethodologyPropagation of Hazard to sitePropagation of Hazard to site

Estimation of Loss

Estimation of Loss

Hazard DefinitionHazard Definition Estimation of Damage

Estimation of Damage

• HurricaneStorm Tracks

• HurricaneSurface Wind Speed

• Vulnerability functions

Occupancy

• Loss dataValidated with historical loss dataStorm Tracks

• EarthquakeMagnitudes

Frequencies

Surface Wind Speed

Local Wind Gust

• EarthquakeAttenuation functions

Occupancy

Building age

Number of stories

Construction materialeque c es

Soil MapsConstruction material

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Exposures Follow Population Density


Source: Wikipedia

Hazard: Historical Hurricanes in Mexico

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Black=tropical depression, Green=Tropical Storm, Yellow=Hurricane, Red= Intense Hurricane (Cat 4, 5)

(Data: (Caribbean: 1851-present;Pacific: 1949 – present)

Intense Hurricanes only

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18 Cat 4/5 landfalls160 years (~0 11 / year)

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12 Cat 4/5 landfalls60 years (~0.2 / year)

160 years ( 0.11 / year)

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Latin America Seismicity

IPGH ifi d th kIPGH unified earthquake catalog from 1800-2003Earthquakes defineEarthquakes define major tectonic boundariesEarthquake parameters:

– Magnitude

– Epicenter locationEpicenter location

– Hypocenter depth

– Special studies of significant earthquakes


earthquakes

Types of Seismic Sources

SubductionInterface

Shallow InterfaceBackground

Shallow Crustal

Deep Crustal

Deep Interface Wadati-Benioff

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Background

Earthquake Recurrence Relations

Type of Relation Type of Distribution Type of Sources Type of Relation Type of Distribution Type of Sources

Exponential Gutenberg-Richter (abbreviated GR)

Area sources & background ( )

(log N = a – bM) g

seismicity Characteristic Faults: Gaussian (bell

f h l) Faults where

ti curve from school) Subduction interface: uniform (box)

recurrence times are available (Type A)

Exponential & Characteristic

33/67 weighted GR/characteristic

Faults where only slip-rate data are available (Type B)


available (Type B)

Time-Dependent Recurrence

C diti l b bilit th t th k ill d iConditional probability that an earthquake will occur during a specified period of time in the future given that one has occurred at a known date in the past (i.e., “the earthquake cycle”)cycle )Accounts for the effects of secular strain accumulation and release (“the elastic rebound theory”)Updated from study by Stuart Nishenko (1991), who at the time was a seismologist with the US Geological Survey

– Updated with earthquakes to 2003

– Updated with IPGH unified earthquake catalog


Faults Modeled as Time Dependent

Subduction ZonesMexicoCentral America

Crustal Fault ZonesCaribbean-N. American plate boundary (Guatemala)Central America

PeruChile*

Antarctic-S. American plate boundary (Tip of S. America))

*Time dependent frequencies near Chile being updated July 2010


Time Dependence: Secular Strain AccumulationAccumulation

Lognormal probability Conditional Probability = A/(A + B)Conditional Probability = A/(A + B)

distribution of recurrence time– Conditional on elapsed time

since last eventLastLast

AA

BB

– Probability increases as strain accumulates & elapsed time increases

LastEarthquake

LastEarthquake

• Probability depends strongly on standard deviation (σ)

Decreaseswith σ

on standard deviation (σ)Larger standard deviation leads to smaller effectTypical value is 0.5

Increaseswith σ

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yp

Exceedence Probability Curve:Hurricane

1.00

Hurricaneab

ility

20% to 50% annual probability of experiencing losses as large as from hurricane Wilma

0.10

danc

e Pr

oba

R f 200

0.01

nnua

l Exc

eed Repeat of 2005

Hurricane Wilma($0.40-$0.50 bn loss)

0 00

An


0.000 2 4 6 8 10

Nationwide Ground-Up Damage, USD billions

Exceedence Probability Curves:Earthquake and HurricaneEarthquake and Hurricane

11.00

100.10

rsbabi

lity

turn

Per

iod,

yea

Exce

edan

ce P

rob

Earthquake

Hurricane1000.01

Ret

Ann

ual E Hurricane

1% to 2% annual probability of experiencing losses as large as from the '85 quake

1,0000.00

0 20 40 60 80 100

Repeat of 1985 Quake:$15 to $25b Losses


Nationwide Ground‐Up Damage, USD billions

Michoacan Earthquake, M8, 19 Sep 1985

We learn from collapses……and we learn…and we learn equally from buildings that remain intact


Photos courtesy of EQE Incorporated EQ Reconnaissance report, 1985

Simulation of Shaking in Mexico City

Th f i t h ki iThe zone of intense shaking in this simulation of the 1985 quake reflects the shape of ancient Lake Texoco

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10 km 10 km300 km

Business Interruption

Function of % damage to structure & contents.Economic loss has to be defined by metrics such as revenue or gross profit margin.BI is calculated at damage level, so policy deductibles and limits can be modeled.Loss Amplification is based on estimates of supply and demand of labor and materials Calculation is done on an event by eventof labor and materials. Calculation is done on an event by event basis- i.e. no factor is applied.Confidence in modeled BI is lower than PD and Contents since detailed BI coverages are not usually modeled and claimsdetailed BI coverages are not usually modeled and claims settlement can be difficult to predict.Contingent BI ( damage to assets not owned) is more challenging to model but in some cases, critical to the insured- e.g. GOM oil


and gas producers. In these cases, network models are used.

Using Portfolio Models for Specific Risks

Geocoding (i.e. CRESTA)Variability of hazard within CRESTALocalized site conditions- e.g. coastal site withLocalized site conditions e.g. coastal site with surge/flooding or soils liquefiable under earthquake.Variability in single site building characteristics.Limits on single site policy sub limits and policy limitsLimits on single site policy sub limits and policy limits.Notwithstanding limitations, can be used as first level risk estimate, especially when risk data is not detailed enough for detailed modelsfor detailed models.


Non proportional modeling

Modeling starts with ground up damage estimates, followed by application of primary coverages.Mexico model supports– Primary policy conditions: deductibles, location, co-insurance,

sub limits

– Reinsurance policy conditions: Semi-automatic bothReinsurance policy conditions: Semi automatic both proportional and non-proportional and Cat XL, with reinstatements

– No facultative


Return Period

For each peril, risks are calculated using event frequencies and underlying frequency (recurrence) models:– Poisson

N ti Bi i l– Negative Binomial

– Exponential

– Characteristic

Conditional probability (e g time dependent)– Conditional probability (e.g. time dependent)

150,000 years of losses calculated for each peril based on above event frequencies, resulting in loss probability per yearReturn period is inverse of loss probability per year but can beReturn period is inverse of loss probability per year, but can be misleading since it implies a steady state recurrence assumption.Most insurers and reinsurers use return periods of 100 to 250 years for hurricane and EQ. (CEA uses 500-1000 years)


years for hurricane and EQ. (CEA uses 500 1000 years)

Modeling non-common structures

Structural and architectural considerations: height, design criteria, building materials, quality of constructionPerformance in other cat eventsIndentify major failure modesMap to building types in modelAdjust vulnerability using quality factors in modelAdjust vulnerability using quality factors in modelUsually assume constructive total loss with damage>50%


Model vs Real losses

Limited insured loss information. Major Mexican events include 1985 Michoacan EQ and 2005 Hurricane WilmaEQECAT models based on damage info with limited claims data.Event characteristics created by EQECAT modelingEvent characteristics created by EQECAT modeling specialists- not by clientsBetter info on building valuation, design details, contents needed to improve model estimatesneeded to improve model estimates


Conclusions

Model development keyed to:– Scientific research: historical data

supplemented with numerical modelssupplemented with numerical models

– More claims data

– Better GIS info on risks- locations, construction types, ages, values, contents

Fl d i k d li l d l d d tFlood risk modeling less developed due to greater information needs: GIS elevation data, topographic, hydrology, land use plus localized


g y gyrainfall data

modeling risks of earthquake and hurricane in …...the new eqecat creating value through increased...

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