Gero Michel - Chaucer

1. Gero Michel, PHD., MSc., adjunct Prof. Western University, London Ont., Managing
Director, Chaucer Copenhagen Office
James Daniell, PHD. MSc., General Sir John Monash Scholar, Karlsruhe Institute of
Technology, Germany, CEDIM Researcher, CATDAT Founder
Parametric Risk Transfer Products,
”Non-conventional” Solutions for Nat. Cat. Risk

2. „ Protection“ Gap, Insured vs.
Economic Risk
High penetration, Fire, Wind (EU, US, JP, AU, NZ, CN), Flood (UK, CEE)
Not covered: Infrastructure, Emerging markets (partly), developing markets
(<5%), Flood many areas, earthquake...
Insured AAL, USD 75bn Economic AAL, USD 350bn
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X2

3. • Cash Flow: Delay in payment for indemnified losses may
lock in capital at high cost (NZ Eqs, HU Irma, other)
• Insurance infrastructure might be weak (protection
gap)
• Indirect losses (loss of profit etc.), sanctions, other may
hinder solutions or lack well integrated loss models
• Missing Transparency, Black Box Models, Model
Uncertainty, Change, current solutions might not be
sufficient (e.g. temperature correlation to energy consumption
>95%)
The Challenge …
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4. Has lead to:
a) Lack of Risk Transfer Products
b) Low capacity/High Price
c) Delayed pay-out hindering business
continuity
The Challenge …
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5. • Intensity in a Box, indicators such as temperature,
pressure, precipitation, ground acceleration, windspeed etc.
• Other, e.g. Economic Indicators
• Loss in a Box, actual/modelled
• Box can range between “large” box (low res., e.g. 10s to 100s of
km), nested solutions, to high res. grid
• Combinations of triggers (indicators, modelled, indemnity)
• Fixed indicator (ex-ante) or modelling agent (ex-ante and
ex-post)
Triggers for Parametric
Products
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6. Reduced Basis Risk
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M=7.4from to
Intensity in a box,
High basis risk
Low Resolution
Inflexible
Risk not well understood
Intensity in a grid
Low Basis Risk
High Resolution
Flexible
Risk well understood
M=8
Pre-calculated
losses all
intensities (e.g. steps of
0.1) for each grid cell
Odds for selected
intensities

7. Iran EQ Trigger Product
Risk/Trigger product independent of underlying indemnity
losses for Iran (sanctions) such that
a)Basis risk is minimized
b) Loss trigger can be calculated using a method that is flexible,
accurate, auditable, and can be used Ex-ante or Ex-post
In order to do this the product has been:
1) Based on a detailed earthquake risk model for Iran
2) Includes (high resolution) industry exposure
3) Future as well as past earthquake Losses
4) Connects future earthquakes to tradeable products
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8. Fault line
Modelling
IRAN EARTHQUAKE HAZARD MODEL
COMPONENTS
Complex Area
Source
Modelling
Shallow Area
Source
Modelling
Point Source
Modelling
Follows historical earthquakes, geology, tectonics, & fault regimes
Two modes – 1) from the original area source model of GSHAP; and 2) from
the latest modelling in the Middle East (EMME) project. Results compared to
Chaucer internal Iran risk model
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9. EARTHQUAKE CATALOGUE FOR
IRAN
Stochastic event set (right). Over 720,000 earthquakes in a 100,000
year stochastic event set with M>=5.5
All possible combinations of earthquakes with sufficient occurrences for a
reasonably stable PML/Risk curves and high resolution trigger
product
Zare et al. IIEES, internal mod.
Historical EQs Stochastic Event-set
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10. • Exposure: Disaggregation of
available Insurance data
• Distribution by population and capital
stock density per province
• Uses settlement footprints and census
data
• Building types from construction statistics
and census
• Detailed analysis of portfolio likelihood at
a point
• Vulnerability: Calibrated functions
based on Iranian typologies
• 8 basic vulnerability functions for
insurance portfolios from publications
• Functions consistent with uncertainty
associated with buildings
Exposure Model &
Vulnerability Models
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11. Trigger Product Iran
• Detailed earthquake in a box
trigger with all magnitude events
revealing losses
• 10km by 10km grid-cells chosen
with the hypocentre within each cell
triggering loss payment (large # of event
years for covering uncertainty),
• Ex-ante: Earthquake losses
calculated for all possible
magnitudes between 5.5 and 8.5
in steps of 0.1 for each cell.
Layer 0 1 2 3 4 5 6 7 8 9 10 11
Priority (bill. IRR) 0 450 1200 2400 4800 9600 15000 21000 28000 34000 40000 45000
5.5 630.538% 5.308% 1.985% 0.935% 0.611% 0.090% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%
5.6 507.440% 5.184% 1.945% 0.963% 0.526% 0.096% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%
5.7 408.642% 5.686% 2.054% 1.064% 0.503% 0.240% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%
5.8 328.495% 5.969% 2.151% 0.962% 0.485% 0.268% 0.066% 0.000% 0.000% 0.000% 0.000% 0.000%
5.9 264.084% 6.185% 2.072% 1.016% 0.501% 0.264% 0.141% 0.038% 0.000% 0.000% 0.000% 0.000%
6 211.872% 6.646% 2.173% 1.086% 0.532% 0.252% 0.148% 0.041% 0.000% 0.000% 0.000% 0.000%
6.1 169.924% 7.041% 2.325% 1.027% 0.526% 0.255% 0.166% 0.077% 0.033% 0.000% 0.000% 0.000%
6.2 135.920% 7.635% 2.348% 1.071% 0.549% 0.294% 0.175% 0.120% 0.060% 0.026% 0.018% 0.000%
6.3 108.339% 8.464% 2.504% 1.136% 0.550% 0.278% 0.174% 0.107% 0.087% 0.042% 0.018% 0.015%
6.4 86.330% 9.505% 2.698% 1.140% 0.521% 0.239% 0.164% 0.101% 0.080% 0.064% 0.034% 0.031%
6.5 68.410% 9.896% 2.831% 1.192% 0.505% 0.254% 0.173% 0.113% 0.070% 0.059% 0.046% 0.027%
6.6 53.986% 9.831% 2.958% 1.243% 0.546% 0.241% 0.153% 0.100% 0.078% 0.050% 0.040% 0.038%
6.7 42.119% 9.198% 3.146% 1.257% 0.534% 0.230% 0.143% 0.100% 0.064% 0.059% 0.038% 0.037%
6.8 32.819% 8.385% 3.274% 1.289% 0.508% 0.219% 0.140% 0.103% 0.064% 0.048% 0.045% 0.030%
6.9 25.117% 7.296% 3.299% 1.288% 0.491% 0.197% 0.126% 0.085% 0.061% 0.041% 0.031% 0.026%
7 19.033% 6.127% 3.094% 1.247% 0.475% 0.197% 0.106% 0.072% 0.052% 0.039% 0.028% 0.024%
7.1 14.284% 5.010% 2.810% 1.276% 0.474% 0.178% 0.095% 0.064% 0.046% 0.038% 0.032% 0.025%
7.2 10.343% 3.959% 2.355% 1.235% 0.461% 0.165% 0.096% 0.057% 0.043% 0.037% 0.032% 0.028%
7.3 7.407% 3.058% 1.955% 1.132% 0.425% 0.159% 0.081% 0.048% 0.033% 0.026% 0.024% 0.023%
7.4 4.946% 2.165% 1.498% 0.929% 0.397% 0.140% 0.069% 0.042% 0.025% 0.022% 0.018% 0.016%
7.5 3.151% 1.418% 1.044% 0.660% 0.319% 0.108% 0.060% 0.033% 0.021% 0.019% 0.018% 0.016%
7.6 2.102% 0.945% 0.735% 0.482% 0.258% 0.089% 0.047% 0.024% 0.018% 0.010% 0.010% 0.010%
7.7 1.255% 0.577% 0.467% 0.325% 0.169% 0.062% 0.030% 0.015% 0.008% 0.008% 0.005% 0.004%
7.8 0.603% 0.266% 0.216% 0.147% 0.077% 0.029% 0.014% 0.010% 0.004% 0.004% 0.003% 0.003%
7.9 0.321% 0.127% 0.104% 0.082% 0.045% 0.020% 0.013% 0.007% 0.005% 0.003% 0.003% 0.003%
8 0.154% 0.046% 0.043% 0.038% 0.023% 0.010% 0.006% 0.005% 0.002% 0.001% 0.001% 0.000%
8.1 0.086% 0.022% 0.020% 0.018% 0.007% 0.003% 0.001% 0.001% 0.000% 0.000% 0.000% 0.000%
8.2 0.055% 0.009% 0.008% 0.008% 0.004% 0.001% 0.001% 0.001% 0.000% 0.000% 0.000% 0.000%
8.3 0.035% 0.003% 0.002% 0.002% 0.001% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%
8.4 0.023% 0.002% 0.001% 0.001% 0.001% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%
8.5 0.017% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%
RECENT EVENT IN
IRAN (Mw7.2/7.3)
Indicated loss matched
portfolio loss (stayed
within considered 1st
layer)
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12. Parametric EQ Trigger Product,
Central Asia
• Parametric EQ Trigger
covering Central Asia plus
Azerbaijan, Armenia, and Georgia
• Product based on trigger grid
with pre-calculated
industry/company losses
for all magnitudes between 5.5
and 8.5, spacing of 10-20km
• Pay-out defined by location, zone,
country, or group of countries
• Trigger Loss based on EQ
magnitude magnitudes in steps of
0.1
• Premium/fee as multiple of
expected loss calculated for the
trigger product (underlying model)
Providing grid updates (exposure) annually (i.e. losses
for each magnitude by country and for all groups of
countries, Risklayer as modelling agent
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13. • Pan-Central Asian EQ Model
• Local or regional parametric trigger products
• Local/Regional Vulnerabilities
• High Resolution Economic or specific Exposure
• Local/Regional Hazard
• Pan-Central Asian EQ event set
Basic EQ
event set
”Hazard,
Source Zones,
Max. Eqs,
Grounds acc.
Exposure,
enforecement
of code
Trigger Products Based on Modelled
Losses, EQ Model based on Daniell & Schaefer (2014)
Vulnerability
Stats. layers
Modelled
Results
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14. Economic Losses Over Time
Normalized historical losses
considering exposure as well as
vulnerability change!
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15. Conclusion/Outlook
Parametric/index products might help:
Cash-Flow - minimize capital cost – capital market products
Higher Transparency (openly discussed index or published model)
Fill the Protection Gap
Dual trigger products for swift pay-out after loss
Enlarge Ecosystem of modelling/Analytics agents
Explosively growing Sensor products (digital homes) – Quasi-real time losses
Crowdsourced exposure may help Avoid emerging GDPR issues
(independent loss triggers)
Trading on future “inter-markets” platforms with index products
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