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Use of Cat-Multi-Models for the Insurance Industry. Gero Michel. Conflicting Objectives: Commercial strategy: based on generating value short (to medium) term Inter-annual Variability: Many opportunities might not be profitable for one year

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  • Conflicting Objectives:

  • Commercial strategy: based on generating value short (to medium) term

  • Inter-annual Variability: Many opportunities might not be profitable for one year

  • Diversification: The insurance world is too small to diversify cat risk away

  • History based: might not be sufficient to forecast the future

  • Short-term needs: Cat Models are in general long-term

  • “Accountable”: Avoid the outsized loss?

  • “Opportunity”: Outsource your brain to the consensus?



  • Risk Tolerance: market?

  • Four types of companies:

  • Risk Averse

  • Risk Taking

  • Analytical/Managing

  • Pragmatists


Only the market?Analysts and Pragmatists might want to use models (top-down or bottom up) but

Only the Analysts consider Multi- Modeling necessary (without being further incentivized)



Catastrophe models defined el for almost any stretch peril territory
Catastrophe Models: Defined EL for Almost any Stretch, Peril & Territory

10,000 yrs statistical

300 yrs GCM

“Trials of Stochastic event sets” limited by “ knowledge, computer power, and imagination”,


  • Collectively induced & Territory: 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
Assume we can Avoid the “Sameness”, can Find the Upside, and Define Model Skill/Accuracy

1000 yrs stochastic

50 yrs history

In-house stochastic crustal EQ catalogue

Major available models however based on consensus hazard views: HERP, USGS, outsource your brain and accountability…


Consider 2 sets of model results
Consider 2+ sets of model and Define Model Skill/Accuracyresults

Model of choice for any territory or peril

Average: Include two or more sets and divide event likelihood by number of sets

Event match and complement: adjust activity rates

Alter individual events, match, complement, and adjust activity rates

Hazard

Vulnerability

Risk assessment

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… what about and Define Model Skill/Accuracy:

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, and

There might or might not be trends/regimes etc.

…black swans?


Regimes dynamic allocation of capital
Regimes & Dynamic Allocation of Capital and Define Model Skill/Accuracy

ChangingRegimes

NOAA Hurdat reanalysis: Storms in a box since 1851


The skill of forecasting cutting through to science
The Skill of Forecasting, and Define Model Skill/AccuracyCutting Through to Science


Towards: and Define Model Skill/Accuracy 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


Beyond expected loss pricing the known known unknown known
Beyond Expected Loss: and Define Model Skill/AccuracyPricing the known known, unknown known…

Peter Taylor, 2009


Beyond expected loss pricing the known known unknown known1
Beyond Expected Loss: and Define Model Skill/AccuracyPricing the known known, unknown known…

Peter Taylor’s Rumsfeld Quadrants

Peter Taylor, 2009


Beyond expected loss pricing the known known unknown known2
Beyond Expected Loss: and Define Model Skill/AccuracyPricing 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!


Willis research network at the end of 2009
Willis Research Network at the End of 2009 and Define Model Skill/Accuracy

Uncertainty, clustering, statistical modelling

Catastrophe risk financing / public policy

Environmental modelling, GIS, Remote Sensing

ERM, operational risk and financial modelling

Planning policy, vulnerability

Hydrology, spatial statistics

Urban flooding, meteorology

Risk assessment, seismic risks, earth observation

Storm surge, sea level rise

Flood modelling and data

Vulnerability, seismic risk, remote sensing

Geological risks, groundwater flooding

Asia-Pacific geohazards

Climate risks and modelling

Flood hydraulics, high performance computation, expert elicitation

Climate risks, modelling

Seismic risk, risk appetite

Climate risks, hail risk, vulnerability, seismic risk

Flooding, pollution

Financial modelling, cost of capital

Visualisation, informatics, risk communication

Vulnerability, infrastructure

Demand surge, vulnerability

Climate and extreme weather, modelling

Climate modelling, extreme weather

Remote sensing, satellite data

Climate drivers of extreme events, uncertainty

Climate risks

Climate risks, flooding

Tsunami

Geospatial data / systems

17


History of wrn
History of WRN and Define Model Skill/Accuracy

2nd Int. Climate Risks liaison group

Official

launch

Int. Geospatial liaison group

2007

2008

2009

2010

1st annual Global Clients Meeting

Bermudan reinsurers meeting

2nd annual Global Clients Meeting

Bermudan reinsurers meeting

European reinsurers meeting

3rd annual Global Clients Meeting

Bermudan reinsurers meeting

European reinsurers meeting

2010:

Beijing Normal University

Bogazici University

GFDL

Newcastle

Oklahoma

UNAM

Universidad de Los Andes

UWI

Wharton, U Penn

CEE flood v2.0

GCM TC track

Hybrid Quake

V1.0 (Tunisia)

Demand surge methodology

Cat

Indices

(e.g. WHI)

ETC Clustering

CEE Flood v1.0

Members per annum

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Wrn challenges and opportunities
WRN Challenges and Opportunities and Define Model Skill/Accuracy

  • Extremes:How much is random, what can be learned?

  • The Next Year; How relevant is the long-term average?

  • Actualistic Principle: Is history sufficient to predict future losses?

  • Nutshell numbers: Do we “Make everything as simple as possible, but not simpler” (Albert Einstein)?

  • Change: How do we cope/create opportunities with change?


Key research 2010
Key Research 2010 and Define Model Skill/Accuracy

Overarching research projects

Demand Surge–Colorado University

Business Interruptionand 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

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

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Managing extremes insurance decision making
Managing Extremes & Insurance Decision Making and Define Model Skill/Accuracy

  • 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


Climate change climatewise wrn
Climate Change and Define Model Skill/AccuracyClimateWise, 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


Conclusion
Conclusion and Define Model Skill/Accuracy

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?

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