Download Risk Assessment Global Case Study: Catastrophe Loss Modelling

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Risk Assessment Global Case Study:
Catastrophe Loss Modelling
Dr. Robert Muir-Wood
Chief Research Officer
Risk Management Solutions
©2014 Risk Management Solutions, Inc.
|
Confidential
THE ORIGINS OF
CATASTROPHE
MODELLING
History does not contain a sufficient population of catastrophes from which to
derive a stable mean loss
Or address questions like - what loss can we expect once in a hundred years
(on average)
So we have to create a richer (‘100,000 year’) set - through generating a
large population of virtual catastrophes
 Each event must be credible
 Each event has a probability
 And the whole population has to be a complete representation of the
‘universe of possible events’
 How do we do this?
CREATING THE
SYNTHETIC
CATALOGUE
Research the best historical catalogue
Understand catastrophe historiography
Thresholds of completeness by period and region
Event prehistory (as from geological evidence)
Recognise that history is only one realisation of the possible
Most synthetic catalogues are some hybrid of:
•
Statistical Methods
•
Dynamical Methods (physics based models)
Look for independent measures for calibration – such as regional strain rate
for earthquakes or river flows as independent of rainfall extremes.
Framework for Earthquake Catastrophe Loss Modelling
Generate Stoch.
Events
Earthquake
Ground motion
Apply
Exposure
Calculate
Damage
Quantify
Financial Loss
In Major Catastrophes - Loss does not occur in isolation
Political Context
Economic Context
Total Event Impact
90%
$ Loss
Stochastic
Hazard
Vulnerability
Claiming
Financial
Loss
Annual Probability of Exceedance
Quantifying Catastrophe Risk: the Exceedance Probability (EP)
Curve Exposure = € 1,000 B
Event ID
Event 1
Event 2
Event 3
Event 4
Event 5
Event 6
Event 7
...
5.0%
4.0%
3.0%
2.0%
1.0%
€ 20
€ 40
Annual
Cumulative AAL(M)
Loss M Probability
Rate
0.01%
€ 0.01
0.01%
€100
€ 50
0.51%
€ 0.25
0.5%
€ 40
0.81%
€ 0.12
0.3%
€ 25
0.65%
1.46%
€ 0.16
€ 20
2.36%
€ 0.18
0.9%
€ 15
3.36%
€ 0.15
1.0%
€ 12
4.86%
€ 0.18
1.5%
...
...
Total € 1.05
Loss
€ 60
€ 80
€ 100
Visual Display of EP Curves & Return Period Losses
• The EP curve provides a visual interpretation of loss potential
• Each point of the curve has an associated threshold and probability
of exceedance
FLOOD DRIVES
80% OF AAL
Yangtze and Pearl
River Delta economic
zones are high risk
YANGTZE
RIVER DELTA
PEARL RIVER
DELTA
Economic
Zones
©2014 Risk Management Solutions, Inc.
Confidential
MANAGING
TSUNAMI RISK
IN CHINA
©2014 Risk Management Solutions, Inc.
Confidential
CATASTROPHE MODELS
DRIVE THE BUSINESS OF
CATASTROPHE INSURANCE
Underwriting
& pricing
Accumulation
Control
Portfolio
Management
Capital
Allocation &
Regulatory
Reporting
Reinsurance
Pricing &
Structuring
Alternative
Risk Transfer
Event
Response
THE ‘SECRET
SAUCE’ OF CAT
MODELLING
• Needs strongly interdisciplinary approach (Science, Engineering, Statistics,
Insurance) – RMS employs c 100 PhDs
• High value for global insurance industry based on risk diversification regulatory reporting requirements and investor disclosure – requires
‘industrial strength’ commercial Cat modelling capability and long term model
maintenance
• The insurance industry is collecting key ‘scientific’ data on exposures and
loss. However claims data are also proprietary (and represent competitive IP).
Working with/for insurance industry – claims and loss data are utilized to
improve vulnerabilities and test short RP modeled losses.
• The key to robust catastrophe modelling is multiple rounds of calibration
Optimization of Portfolio Composition
Return
Efficient Frontier
Risk Free
Return
Risk
• Each dot represents a portfolio
Mitigating the Risk in a Supply-Chain Network
AA EDT
100 yrs
500 yrs
BI EDT
.02
.02
.02
.01
.01
.01
05
05
05
8.3
CBI ratio
31.5
2.89
2.25
CBI ratio with Inventories
1.75
1.91
2.06
1.62
0
0
0 0
0
0
Inventory 60days
Supplier C
(Semiconductor)
Facility
(General Assembly)
Suppliers B
(Body)
Facility
Facility
Facility
Supply Chain
Supply Chain
Supply Chain
Supply Chain with
Supply Chain with Inventories
Supply Chain with Inventories
Inventories
0.02
Annual Probability of Exceedance
15
15
15
0.36
0.015
Suppliers A
(Engine)
0.01
0.005
10
010
10
0
20
20
20
10
30
30
30
20
40
40
40
30
50
50
50
40
60
60
60
50
EDT (days)
70
70
70
60
80
80
80
70
90
90
90
80
90
Suppliers
A
Suppliers
B
Supplier
C
13
In ven tory
60d ays
Facility
Framework for Probabilistic Earthquake Casualty
Modelling
Generate Stoch.
Events
Ground motion
Footprints
Damage &
Collapse
Building Stock
inventory
Human 24 hr
Exposure
Casualties
Framework for Storm Surge/Tsunami Evacuation
Modelling
Stochastic
Flood
Footprints
Human 24 hr
Exposure
Evacuation
Model
Casualties
RMS TIME
STEPPING SURGE
MODELLING FOR
50,000 EVENTS IN
US HURRICANE
MODEL
REGIONAL MESH
©2014 Risk Management Solutions, Inc.
TRACK SET
WIND FIELD
LOCAL MESH
FLOOD DEPTHS
Confidential
All US, All Lines, Ground Up Loss
Billions
RISKY BUSINESS 2014
COASTAL US ‘INSURABLE’ LOSS UNTIL 2100
200
Wind loss only
MSL rise /
surge
range
CMIP5
RCP4.5/8.5
model
range
CMIP3
A1B
model
range
Wind Only - Historical Activity
180
Wind & Surge (Full) - Historical Activity
Wind Only - CMIP5 (RCP4.5)
160
Wind & Surge (Full) - CMIP5 (RCP4.5)
Wind Only - CMIP5 (RCP4.5) -Active Phase Regionalisation
140
Wind & Surge (Full) - CMIP5 (RCP4.5) - Active Phase Regionalisation
120
100
www.riskybusiness.org
80
60
40
20
2010
2030
2050
2070
Year
©2014 Risk Management Solutions, Inc.
Confidential
2090
2110
2130