Post on 21-Jan-2016
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Use of Cat-Multi-Models for the Insurance IndustryGero Michel
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Conflicting Objectives: Commercial strategy: based on generating value short (to medium) termInter-annual Variability: Many opportunities might not be profitable for one yearDiversification: The insurance world is too small to diversify cat risk away History based: might not be sufficient to forecast the futureShort-term needs: Cat Models are in general long-term“Accountable”: Avoid the outsized loss?“Opportunity”: Outsource your brain to the consensus?
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Why is there so little interest in analytics/ERM in our market?
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Risk Tolerance:Four types of companies: Risk Averse Risk Taking Analytical/Managing Pragmatists
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1. Only the Analysts and Pragmatists might want to use models (top-down or bottom up) but
2. Only the Analysts consider Multi- Modeling necessary (without being further incentivized)
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Solvency II/Regulator: Likely to ask for Multi-Modeling/Near Term
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Catastrophe Models: Defined EL for Almost any Stretch, Peril & Territory
5 to 5 .5 5 .5 to 6 6 to 6 .5 6 .5 to 7 7 .5 to 8 8 to 8 .3
“Trials of Stochastic event sets” limited by “ knowledge, computer power, and imagination”,
300 yrs GCM10,000 yrs statistical
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Collectively induced: Models are rejected in case they do not match expectation
Believe: The “big thumb” is as good as any one model
Value of model: lies in the disaggregation of risk, Pricing and Portfolio management
Assume we can Avoid the “Sameness”, can Find the Upside, and Define Model Skill/Accuracy
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5 .5 to 7 7 to 7 .5 7 .5 to 8 8 to 9
In-house stochastic crustal EQ catalogueMajor available models however based on consensus hazard views: HERP, USGS, outsource your brain and accountability…
1000 yrs stochastic50 yrs history
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Consider 2+ sets of model
results1. Model of choice for any
territory or peril2. Average: Include two or
more sets and divide event likelihood by number of sets
3. Event match and complement: adjust activity rates
4. Alter individual events, match, complement, and adjust activity rates
Risk assessmentHazard Vulnerability
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… what about: High chance that “Average” does not explain the next year“History” does not explain future “Consensus” is unlikely to explain the “common” outlier, Basins are not independent, andThere might or might not be trends/regimes etc.…black swans?
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Regimes & Dynamic Allocation of Capital
NOAA Hurdat reanalysis: Storms in a box since 1851
Changing Regimes
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The Skill of Forecasting,Cutting Through to Science
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Towards: Ensemble set including wide range of short-term and long-term results allowing decision making skewed to company strategy and risk tolerance
Peter Taylor, 2009
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Beyond Expected Loss: Pricing the known
known, unknown known…
Peter Taylor, 2009
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Beyond Expected Loss: Pricing the known known,
unknown known…
Peter Taylor, 2009
Peter Taylor’s Rumsfeld Quadrants
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Beyond Expected Loss: Pricing the known known,
unknown known…It is as bad to over-estimate risk as
it is to under-estimate it as both involve a cost… (D. Apgar, 2006).
Loading is actually not N.N. Taleb’s idea!
…Peter is not an UW… by the time we reach the unknown unknowns
the deal is gone for us!
It is as bad to over-estimate risk as it is to under-estimate it as both
involve a cost… (D. Apgar, 2006).Loading is actually not N.N. Taleb’s
idea!…Peter is not an UW… by the time we reach the unknown unknowns
the deal is gone for us!
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Willis Research Network at the End of 2009
Environmental modelling, GIS, Remote Sensing
Planning policy, vulnerability
Hydrology, spatial statistics
Vulnerability, seismic risk, remote sensing
Geological risks, groundwater flooding
Flooding, pollution
Visualisation, informatics, risk communication
Demand surge, vulnerability
Flood hydraulics, high performance computation, expert elicitation
Climate drivers of extreme events, uncertainty
ERM, operational risk and financial modelling
Flood modelling and data
Risk assessment, seismic risks, earth observation
Climate risks, hail risk, vulnerability, seismic risk
Seismic risk, risk appetite
Climate and extreme weather, modelling
Remote sensing, satellite data
Climate modelling, extreme weather
Climate risks, flooding
Geospatial data / systems
Catastrophe risk financing / public policy
Asia-Pacific geohazards
Urban flooding, meteorology
Storm surge, sea level rise
Climate risks and modelling
Climate risks, modelling
Financial modelling, cost of capital
Climate risks
Vulnerability, infrastructure
Tsunami
Uncertainty, clustering, statistical modelling
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History of WRN
2007 2008 20091st annual Global Clients Meeting
3rd annual Global Clients Meeting
Official launch
ETC Clustering
CEE Flood v1.0
Demand surge methodology
2nd annual Global Clients Meeting
2010
2010:Beijing Normal UniversityBogazici UniversityGFDLNewcastleOklahomaUNAMUniversidad de Los AndesUWIWharton, U Penn
CatIndices(e.g. WHI)
CEE flood v2.0
GCM TC track
Hybrid QuakeV1.0 (Tunisia)
Int. Geospatial liaison group
European reinsurers meeting
Bermudan reinsurers meeting
Bermudan reinsurers meeting
Bermudan reinsurers meeting
European reinsurers meeting
2nd Int. Climate Risks liaison group
0
5
10
15
20
25
30
35
40
45
2006 2007 2008 2009 2010
Pa
rtn
er
Ins
t.
Members per annum
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WRN Challenges and Opportunities1.Extremes: How much is random, what can be learned?
2.The Next Year; How relevant is the long-term average?
3.Actualistic Principle: Is history sufficient to predict future losses?
4.Nutshell numbers: Do we “Make everything as simple as possible, but not simpler” (Albert Einstein)?
5.Change: How do we cope/create opportunities with change?
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Key Research 2010
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Flagship research projects
Hybrid loss model for seismic risks – first of its class: Tunisia
Imperial College, ROSE School Pavia, Cambridge University, Kyoto University, Colorado University
Extreme weather hazard modelling from GCMs:Frequency, Severity, & Change
Walker Institute / Reading University, NCAR Colorado, National University Singapore, Systems Engineering Australia, University of Exeter
Regional flood risk: Central and Eastern European Flood Bologna University, Exeter University, Fluvius
Consulting (Vienna), Bristol University, Durham University, Princeton University , Newcastle
Overarching research projects
Demand Surge –Colorado University
Business Interruption and infrastructural risk - Kyoto University
Risk & Uncertainty Visualisation –City University
Extreme Value Statistics and Uncertainty –Exeter University
Exposure, Post Event Calibration & Remote Sensing –Cambridge University
Urban & Megacity Risk – All members
High Performance Computation – All members
Operational Risk, Cost of Capital and Public-Private Risk Transfer – ETH, Swansea, Wharton
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Managing Extremes & Insurance Decision Making
Global and conceptual
Global and operational
Regional & Local
Inform Existing Models
Create Additional Models where Model Penetration is insufficient
Solvency Margin, Capital Cost, & Rating
Decision Making Under Uncertainty
Alteration & Change, the current vs. future Underwriting Process
Partnering with the world’s most influential decision makers
Sharing best practice and key research outputs to redefine sustainability and shape future development policy
Using knowledge of extremes and climate modelling technology to prepare for environmental change and protect essential resources
Role of Re-insurance on Sustainability and
Managing Extremes
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Climate ChangeClimateWise, WRN
Building public understanding on the importance of Climate Change, and ways to communicate risks and uncertainty in a more balanced way.
Measures for the insurance industry to better support public policy and regulation, e.g. through education at a individual (constituent) level.
How to deal with the non-availability of local level data/projections, that are needed for an effective response of the industry?
The role of insurance in adaptation, particularly the challenges of risk-based pricing and affordability.
What happens if global mean temperature exceed 2°C?
Decision making under deep uncertainty Past not capable of predicting the future
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Conclusion
Our Future is related to multi-modelling und uncertainty subject to risk tolerance/culture of individual companies
Related Challenges include:
Individual model results with respect to range of possibilities?
What is the “best ensemble” for which company?
How do we make decisions/change our process under deep uncertainty?