spatial uncertainty in catastrophe modelling · 2015-07-28 · aon benfield is an industry leader...
TRANSCRIPT
Spatial Uncertainty in Catastrophe
Modelling
Chris EwingChris Ewing
Aon Benfield
� What is Reinsurance?
� What is Catastrophe Modelling?
� Uncertainty in Catastrophe Modelling
� (Spatial) Uncertainty in Cat Modelling
– Hazard
Overview
– Exposure
– Vulnerability
– Loss
� ELEMENTS – Coping with Uncertainty
� Conclusions
� Reinsurance is simply insurance for insurers - it allows the insurer to remove risk
� Natural catastrophes can produce large losses for insurers and are
What is reinsurance?
large losses for insurers and are one type of risk where reinsuranceis needed
� Swiss Re (one of the world’s largest reinsurers) formed after a large fire in Glarus, Switzerland in 1863
� Recent global economic crisis =tighter regulation in banking and insurance on liquidity
� New regulations from EEC called Solvency II seek to reduce the risk of an insurer not meeting claims
Reinsurance in the ‘New Economy’
� Heavy losses from natural disasters need better catastrophe risk assessment
� Solvency II “standard formula‟ for calculating Nat Cat risk - but it is too simplistic
� Aon Benfield is an industry leader in treaty, facultative and capital markets, and act as reinsurance intermediaries and capital advisors
� Within Aon Benfield, Analytics offers clients catastrophe management, actuarial, rating
What do we do?
agency advisory and risk and capital strategy expertise
� Within Analytics, Impact Forecasting develop catastrophe models that help analyse the financial implications of catastrophic events
Risk
Credit Risk
Asset Risk
CatastropheNon
Business Risk Event RiskMarket Risk
Operating RiskLiability Risk
Types of risks faced by an Insurance corporation
•Corporate Bonds
•Reinsurance Ceded
•Derivative counterparties
•Other Receivables
•Equities
•Interest Rates
•Derivative Securities
•Foreign Exchange
•Losses due to Natural Disasters
•e.g. Flood, Windstorm, Earthquake
•Potential claims reported or paid at random times for random amounts
•Changes in volume
•Changes in Margin
•Fraud
•Unintentional Errors
•Systems Interruption
Credit Risk CatastropheNon
- CatastropheBusiness Risk Event RiskMarket Risk
� A Cat Model determines the potential loss to a client’s exposure from natural perils
– wind, earthquake, flooding, fire
� Cat Modelling is a tool/technique to estimate the potential loss to property and life following a catastrophic event
What is Catastrophe Modelling? - I
� 4 components of a Catastrophe Model
– Hazard
– Vulnerability
– Exposure
– Loss
� Spatial uncertainty is present in all components
Hazard
RISK
Peril Frequency and SeverityEQ shaking intensity
Wind strength
Flood depth inundation
Blast radius
Loss
Calculation
What is Catastrophe Modelling? – II
Vulnerability Exposure
RISK Risk Portfolio DataStructure values
Contents values
Time Element
Number of people
Deductibles / Limits
Reinsurance
Hazard SusceptibilityStructural classifications
Occupancy descriptions
Secondary Characteristics
� To help understand the risk faced by corporations to natural catastrophes
� To assist in determining insurance / reinsurance strategy
� Development of cat models following:
– European windstorms 1987 / 1990
Why use Catastrophe Modelling?
– European windstorms 1987 / 1990
– Hurricane Andrew 1992
– World Trade Centre terrorist attacks
– Hurricane Katrina 2005
� Catastrophe Models also cover Terrorism, Pandemic Influenza and Workers Compensation
� Most insurers and reinsurers will have some form of (basic) catastrophe model
� Catastrophe Models are required for Solvency II
� 3 commercial modelling companies
Who uses Catastrophe Models?
� 3 commercial modelling companies (AIR, EQECAT, RMS)
� Aon Benfield Impact Forecasting develops ELEMENTS - our in-house catastrophe model
Impact Forecasting Catastrophe ModelsCountry / Peril Crop
Cyclone /
HurricaneEarthquake
Offshore Oil
PlatformRiver Flood Storm Surge Terrorism
Tornado /
Hail / Severe
Winds
Wildfire /
Bushfire
Workers'
Compensation
Regional Models
Asia ●
Central and Eastern Europe ●
Greece and Cyprus ●
Western Balkans ●
Country models
Albania
Australia ● ● ●
Austria ● ●
Belgium ● ●
Bosnia and Herzegovina ●
Bulgaria ●
Canada ●
Croatia ●
Cyprus ●Cyprus ●
Czech Republic ●
France ● ●
Germany ● ●
Greece ●
Hungary ● ●
Japan ●
Kazakhstan ●
Montenegro ●
New Zealand ●
Norway ●
Poland ●
Romania ●
Serbia ●
Slovakia ● ●
Slovenia ●
South Africa ● ● ●
Switzerland ●
United Kingdom ●
United States ● ● ● ● ● ● ● ● ● ●
� All models are uncertain
� The key is to understand the uncertainty and handle it as best as we can
Vulnerability LossHazard
Event Generation
IntensityCalculation
Incomplete or incorrect data Default assumptions
Application
of limits and
Uncertainty in Catastrophe Modelling
Exposure
Generation Calculation
Risk Characterisation
Damage
Calculation
Policy
Conditions
Insured
LossToo much aggregated data,
insufficiently detailed geographical
position, missing data
of limits and
deductibles
� Due to complexity of nature all models are simplified mathematical representations of physical phenomena
� Primary vs. Secondary
– Primary = Hazard
Uncertainty in Catastrophe Modelling
– Primary = Hazard
– Secondary = Damage
� Aleatory vs. Epistemic
– Aleatory = inherent randomness which cannot be reduced
– Epistemic = uncertainty due to lack of information which can be reduced
� Spatial uncertainty is the lack of, or the error in, knowledge about an object’s geographic position or location
� Location is essential in Catastrophe Modelling to calculate potential losses
(Spatial) Uncertainty in Catastrophe Modelling
Modelling to calculate potential losses
– Where is the Earthquake epicentre?
– Where will the largest intensities be felt?
– Where are the insured locations?
– Where are the flood / tidal defences?
– Where will the tropical storm hit land?
– Where is the postcode which has the
most damage?
� Incomplete geographic data of the hazard
� Incorrect data - inherent errors in the data / data collection errors
� Flooding
– Digital elevation model uncertainty - valley bottoms and streamlines
DTM Comparison - Profile
350
Uncertainty – Hazard I
100
150
200
250
300
350
0 1000 2000 3000 4000 5000 6000
Distance (m)
Ele
vati
on
(m
)
10m FKB Data
25m Mestekart
� USGS - recent Japan EQ (Tohoku) epicentre uncertainty of 8.4 miles
� Hazard location uncertainty has impact on the calculation of intensities etc.
Uncertainty – Hazard II
� Tsunamis area even more problematic to model
– Wave inundation effects
– Effect of vegetation / debris on
wave run-up
– Back-wash
� Building stock - property characteristics or building modifiers
– roof structure
– dwelling type
– construction type
– number of storeys
Uncertainty – Vulnerability I
– number of storeys
– age
� Not enough detail - generally on a regional scale
� Could more accurate data be used?
� Use of Remote Sensing and GIS?……perhaps but costs are too high
Building modifier in
catastrophe model
vulnerability
component
Non-spatial data
(but typically
regional
aggregations)
Ordnance Survey
MasterMap (OS,
2011)
Cities Revealed
Building Class
Dataset (GIG, 2011)
Roof Type Yes No No
Window Type Yes No No
Type (occupancy) Yes Yes Yes
Construction Type Yes No No
Basement property No No No
Uncertainty – Vulnerability II
Basement property No No No
Housing Type
(detached/semi-
detached/terraced)
Yes No Yes
Age Yes No Yes
Building size (area) Yes Yes Yes
Number of stories Yes No No
Typical building modifiers used in a UK based model and
potential datasets
� Exposure = the properties which the insurer covers
� More accurate the address details = closer to actual location in the model
� Flood models especially need accurate addressing
Uncertainty – Exposure I
Province 2-digit postcode (CRESTA) 4-digit postcode
� Compare the 2 examples of a river flood
Uncertainty – Exposure II
Insured property in postcode area
=
affected by the river flood
Insured property building centroid
=
not affected by river flood
� (un)knowledge of the geographical location has the largest impact on accuracy of final losses
Uncertainty - Loss
The impact of improved data on Hurricane
Charley loss estimation
� ELEMENTS developed by Impact
Forecasting is “open-box” unlike other
commercial catastrophe models
� In ELEMENTS, exposure and hazard
uncertainty dealt with by the Master Table - probability of a particular event occurring
ELEMENTS - Coping with Uncertainty I
ELEMENTS
- probability of a particular event occurring
within a particular administrative area
� ELEMENTS deals with uncertainty in
vulnerability by using the Chance of
Loss concept
- the likelihood that a particular
property will be affected by an event
� ELEMENTS also allows the user to select
different vulnerability functions for a given
region to determine the effect of other building
stock information.
� Finally for the exposure component
ELEMENTS allows multiple geographic
ELEMENTS - Coping with Uncertainty II
ELEMENTS allows multiple geographic
resolutions to be used
� US-based models in ELEMENTS can be
matched at building centroid (longitude
and latitude), ZIP Code, County and State
administrative levels.
The ELEMENTS user interface
� There will always be uncertainty in
Cat Models – but we can deal with it
� Location uncertainty has the largest effect on calculated losses
� To communicate uncertainty it is sensible
Conclusions I
� To communicate uncertainty it is sensible
to give a range of potential losses rather
than a single monetary figure
� By understanding the catastrophe modelling
process (re)insurers can better understand
their capital requirements
� ELEMENTS allows uncertainty to be
modelled and understood by allowing each
component of the model to be seen and
modified by the user
� ELEMENTS copes with uncertainty by
allowing a range of probable maximum
Conclusions II
allowing a range of probable maximumloss figures to be generated
� With the advent of increasing regulatory
requirements (such as of Solvency II),
a fully-documented and understandable
catastrophe model is required –
ELEMENTS can provide this
Thank you!
Chris Ewing
Aon Benfield Analytics
Impact Forecasting
t: +44 (0)207 522 8305
e: [email protected]: [email protected]
twitter: @web_gis
References
AIR Worldwide (2010), Understanding uncertainty, http://www.air-worldwide.com/PublicationsItem.aspx?id=19060
AIR Worldwide (2011), http://www.air-worldwide.com/
Aon Benfield (2010), Solvency II for Reinsurance Managers, http://www.aon.com/attachments/reinsurance/201006_solvency_II_reinsurance_managers_full.pdf
Aon Benfield (2011), Information about Aon Benfield, http://www.aonbenfield.com
Aon Benfield (2011a), Impact Forecasting, Aon Benfield, http://www.impactforecasting.com
Aon Benfield (2011b), ELEMENTS: Natural Catastrophe Models documentation, Aon Benfield, 2011
Aon Benfield (2011c), Norway Flood Model Presentation, Aon Benfield, April 2011
Aon Benfield (2011d), Kazakhstan EQ Model Technical Documentation, Aon Benfield, July 2011
Dlugolecki, A. et al. (2009), Coping with Climate Change: Risks and opportunities for Insurers, Chartered Insurance Institute, London/CII_3112
EMSC-CSEM (2011), Mw 9.0 off the Pacific coast of Tohoku, Japan Earthquake, on March 11th, 2011 at 05:46 UTC, http://www.emsc-csem.org/Page/index.php?id=196
EQECAT (2011) http://www.eqecat.com/
FSA (2011), Solvency II, http://www.fsa.gov.uk/pages/About/What/International/solvency/index.shtml, Financial Services Authority, August 2011
References
GIG (2011), Geo-Information Group, Cities Revealed Building Class - http://www.geoinformationgroup.co.uk/products/building-class
Grossi, P. (2004), Sources, nature and impact of uncertainties in catastrophe modelling, 13th World Congress on Earthquake Engineering, http://www.iitk.ac.in/nicee/wcee/article/13_1635.pdf
Holmes, K.W., Chadwick, O.A., Kyriakidis, P.C. (2000), Error in a USGS 30-meter digital elevation model and its impact on terrain modeling, Journal of Hydrology 233 (2000) p154-173
Nurmagambetov A., Mikhailova N., and Iwan W., 1999: Seismic hazard of the Central Asia Region. In: King S. A. et al. (eds.): Seismic Hazard and Building Vulnerability in Post-Soviet Central Asian Republics, p1-43
ONS (2011), http://www.statistics.gov.uk/census2001/profiles/commentaries/housing.asp
OS (2011), http://www.ordnancesurvey.co.uk/oswebsite/products/os-mastermap/
Swiss Re (2011), Swiss Re – Established 1863 - http://www.swissre.com/about_us/established_1863/
RMS (2008), A guide to Catastrophe Modelling, Worldwide Reinsurance, http://www.rms.com/Publications/RMS%20Guide%202008.pdf
RMS (2011), http://www.rms.com/
Rudi, W. and Toksoz, M.N., 2001, Uncertainty Analysis in Seismic Event Location, In: 23rd Seismic Research Review: Worldwide Monitoring of Nuclear Explosions – October 2-5, 2001, p324-322
USGS (2011), Honshu EQ, http://earthquake.usgs.gov/earthquakes/eqinthenews/2011/usc0001xgp/
Zhunusov T., Taubaev A., Itskov I., Mikhailova N., and Nurmagambetov A., 1999: Seismic Hazard and Building Vulnerability in Kazakhstan. In: King S. A. et al. (eds.): Seismic Hazard and Building Vulnerability in Post-Soviet Central Asian Republics, p67–92.