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Severe Convective
Storms
Tornado Alley Ohio Valley
South East
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o Thanks!
Vendor Participation
Model: RQE v14Aug. 2013
Sims: 300 k years
Model: v6.1 - June 2008
Platform: Touchstone v 1.5.2
Sims: 10 k years
Model: US SCS Jan. 2014Platform: RiskLink 13.1
Events: 58 k + high frequency
background
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Comparison Focus &
Exposure Portfolios
Map courtesy of Munich Re
o 2014 Focus
FrequencyAnnual Freq. of $1 loss
Severity - Expected Loss
Location variabilityCoef. Var.
Portfolio Risk - 100 yr. TVar
o 3 Grid Portfolios
~250k grid locations
Residential - $ 200 k Bldg.
o 3 Hypothetical Portfolios
Commercial & Residential
Varied replacement values Bldg., Contents & BI
300 k locations / $ 60 B TIV - total all 3 portfolios
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US Severe Convective Storm
o Modeling Challenges What defines an Event?
How are models calibrated?
What sub-perils are included?
o Risk Management Given the challenges noted above:
How do the models accumulate losses in:
A Single Occurrence?
The Annual Aggregate?
o Model Usage Best practices?
From single location UW to Port. Mgmt.
Whats next?
Any Black Swans?
Initial Background
Questions
Image courtesy of USGS
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o Same
Includes allmodeledsub-perils
oDifferent!
Expectations
Basis
Granularity Approach
SCALES!
Not possibleto map onsame scale!
Frequency
Comparisono Annual Frequencies
Of at least $1 loss
o Relative Scale Left to right - Low to high
*RMS basis differences Combined probabilities & different grid sizes
Probability of loss causing event in grid ANDprobability of risk existing in grid
AIR
EQE
RMS*
Maps by Munich Re
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Frequency Comparison
Southeast & Tornado Alley
AIR EQE RMS*
Kansas
Oklahoma
TN
GAAL
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o Same SCALE!
Includes allmodeled sub-perils
o Different! Granularity
Distribution ofExpectedLosses
AIR Centralized
EQE Oklahoma
RMS Vertical
bands westto east
Ground Up AAL
Comparison
o Relative Scale Left to right - Low to high
AIR
EQE
RMS
Maps by Munich Re
Kansas
Oklahoma
Population density
driven vs. even grid?
96.3
82.0
43.4
Ground Up Annual Expected Loss ($ M)
EQE
AIR
RMS
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GU AAL ($ m) Comparison
South East & Ohio Valley
AIR
23.3EQE
23.3
RMS
28.6
TN
GAAL
KY
INOH
AIR
22.7EQE
24.1
RMS
25.2
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Average Annual Loss
General Observations
o General
Totals more similar than distributions of individual location E[L]
Greatest $ difference in Tornado Alley
o Frequency vs. AAL
Slightly different distributions / concentrations
Implies impact of different peril severities and exposure vulnerabilities
105 4
11
4 3 5
49
33
11
5 5
11
4 3 5
23 21
10 94
15
4 37
58
38
0
10
20
30
4050
60
70
IN KY OH AL GA MS TN KS OK
GU AAL by State ($ m)AIR
EQERMS
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Hail Tornado SL wind
Tornado Alley
EQE
RMS
Hail Tornado SL wind
South East
EQE
RMS
13
Severe Convective
Storms - Sub-Perils
o SCSSub-perils by Grid
Each model includes components for a variety of sub-perils
This years comparison requested location results by sub-perils
Tornado, Hail & Straight Line Wind
o Vendor Submissions
Comparisons difficult
More discussions
in tomorrows
presentations
AIRAll sub-perils
EQEAll sub-perils
Tornado
Hail
RMS
All sub-perils
Tornado
Hail
SL wind
Low Freq. Events
Hail Tornado SL wind
Ohio Valley
EQE
RMS
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o General
High correlation between location values ($) E[L] and Std. Dev.
Selected example using Tornado Alley
Same scale used for each vendor (i.e. 1 for E[L], 1 for Coef. Var.)
Relative Scale
Left to RightLow to High
14
Variability of Location E[L]
Maps by Munich Re
AIR EQE RMS
Kansas
Oklahoma
Cv Cv Cv
E[L] E[L] E[L]
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0
50
100
150
200
250
300
2 20 200
Estimated
Losses($m)
Return Period (yrs)
Ohio Valley Grid All SCS, GU
AIR - AEP EQE - AEP RMS - AEP
AIR - OEP EQE - OEP RMS - OEP
15
Loss Accumulation
Grid EP curves
o General
SCS peril much more likely to have multiple loss occurrences in a year
Risk Metric100 year Tail Value at Risk (TVaR) (estimated by dots on graph)
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0
50
100
150
200
250
300
2 20 200
Estimated
Losses($m)
Return Period (yrs)
Southeast Grid All SCS, GU
AIR - AEP EQE - AEP RMS - AEP
AIR - OEP EQE - OEP RMS - OEP
16
Loss Accumulation
Grid EP curves
o General
SCS peril much more likely to have multiple loss occurrences in a year
Risk Metric100 year Tail Value at Risk (TVaR) (estimated by dots on graph)
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0
50
100
150
200
250
300
2 20 200
Estimated
Losses($m)
Return Period (yrs)
Tornado Alley Grid All SCS, GU
AIR - AEP EQE - AEP RMS - AEP
AIR - OEP EQE - OEP RMS - OEP
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Loss Accumulation
Grid EP curves
o General
SCS peril much more likely to have multiple loss occurrences in a year
Risk Metric100 year Tail Value at Risk (TVaR) (estimated by dots on graph)
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o General
TVaR belongs to a robust and efficient class of risk metrics.
TVaR = Average Loss in the tail of the EP curve beyond a return period
Mapping each grids contribution to the portfolio TVaR is one way to helpvisualize how locations contributed to tail losses.
Contribution TVaR & E[L] can be distributed very differently within any portfolio.
Relative Scale
Left to RightLow to High
18
Allocating Portfolio Risk
to Individual Locations
Maps by Munich Re
AIR EQE RMS
KYIN
OH
KYIN
OH
KYIN
OH
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Next upImpact on
Hypothetical Portfolios
Map courtesy of Munich Re
o Recap
FrequencyDifferent distributions and granularities
E[L] & Variability - Challenges smoothing small footprints, population
based grids, event set sizes and sub-peril assumptions
Portfolio RiskMultiple occurrences more likely in an give year and each
model accumulates loss potential differently
o 3 Hypothetical Portfolios
Commercial & Residential Varied replacement values Bldg., Contents & BI
300 k locations / $ 60 B TIV - total all 3 portfolios
Nominal deductibles - $ 1k & $2k options
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6 6 5
18 17 17
2419
17
GU GR_1k GR_2k
Upper Mid West
AIR EQE RMS
8 7 7
19 17 16
33
2624
GU GR_1k GR_2k
South East
11 10 9
17 16 15
45
3733
GU GR_1k GR_2k
Lower Mid West
20
Hypothetical Portfolio
ALs ($ m)
o Lower Mid West
~73 k locations, $ 8.8 BBldg. values, $ 11.2 B total replacement costo Upper Mid West
~89 k locations, $ 17.8 BBldg. values, $ 23.5 B total replacement cost
o South East
~155 k locations, $ 19.4 BBldg. values, $ 26.3 B total replacement cost
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17
11
Ground Up Annual Expected Loss ($ M)
AIR
EQE
RMS
21
Look Similar?
GeographicDifferences MO
OK & N. TX
So. TX
Sub-Perils Different North to
South?
Terrain Impact?
Other Analysis Granularity
/ smoothing
No. of events /simulated years
Lower
MWo GU AAL
Same scale
o Relative Scale Left to right - Low to high
EQE
Maps by Munich Re
AIR
RMS
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0.3
1.0
0.2 0.1
1.0 1.00.5
1.5
0.6 0.7
1.5 1.71.7
4.7
1.41.0
4.0 4.1
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Masonry Mobile Concrete Steel Unknown Wd Frm
GU Loss Cost (per 1k TIV) by ConstructionAIR
EQE
RMS
0.6 1.0 0.8 0.7
3.7 4.1
1.1 1.2 1.3 0.9
6.4 6.4
2.0
4.53.5
2.7
17.5
15.3
0.0
5.0
10.0
15.0
20.0
IA KS MO NE OK TX
GU AAL by State ($ m)AIR
EQE
RMS
22
Lower Mid West
AAL Breakdown
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Look Similar?
GeographicDifferences Upper WI
Upper MI
Kentucky
SE Ohio
Sub-Perils Different
North toSouth?
TerrainImpact?
Other Analysis
Granularity /smoothing
No. of events/ simulatedyears
Upper
MWo GU AAL
Same scale
o Relative Scale
Left to right - Low to high
EQE
Maps by Munich Re
AIR
RMS
24
18
6
Ground Up Annual Expected Loss ($ M)
AIR
EQE
RMS
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Upper Mid West
AAL Breakdown
2.1
1.00.5
1.2 1.10.6
5.7
4.1
1.2
3.12.6
1.5
7.0
5.0
3.6
2.5
3.8
1.8
0.0
1.0
2.0
3.0
4.0
5.06.0
7.0
8.0
IL IN KY MI OH WI
GU AAL by State ($ m) AIR
EQE
RMS
0.1
0.5
0.1 0.0
0.4 0.40.4
1.1
0.40.5
0.9 1.0
0.5
2.0
0.4 0.4
1.3 1.2
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Masonry Mobile Concrete Steel Unknown Wd Frm
GU Loss Cost (per 1k TIV) by ConstructionAIREQE
RMS
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19
8
Ground Up Annual Expected Loss ($ M)
AIR
EQE
RMS
25
Geographic Differences West of the Appalachians
TN, MS & AL
Virginia
Sub-Perils Different East to West?
Terrain Impact?
Other Analysis Granularity / smoothing
No. of events / simulated years
South
Easto GU AAL
Same scale
o Relative Scale
Left to right - Low to high
EQE
Maps by Munich Re
AIR
RMS
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0.1
0.5
0.0 0.1
0.3 0.30.3
0.8
0.20.4
0.8 0.80.8
1.6
0.50.6
1.41.3
0.0
0.5
1.0
1.5
2.0
Masonry Mobile Concrete Steel Unknown Wd Frm
GU Loss Cost (per 1k TIV) by ConstructionAIR
EQE
RMS
1.1
2.4
0.6
2.11.3
0.6
3.5
6.1
1.1
3.42.6
1.8
6.8
9.0
2.3
5.0 4.8 4.9
0.0
2.0
4.0
6.0
8.0
10.0
AL GA MS NC SC TN
GU AAL by State ($ m) AIR
EQE
RMS
26
Lower Mid West
AAL Breakdown
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0
20
40
60
80
100
2 20 200
EstimatedLosses($m)
Return Per. (yrs)
Southeast Hypoth. Port All SCS, GU,
AEP
AIR EQE RMS
0
20
40
60
80
100
2 20 200
EstimatedLosses($m)
Return Per. (yrs)
Tornado Alley Hypoth. Port All SCS,
GU, AEP
AIR EQE RMS
0
20
40
60
80
100
2 20 200
EstimatedLosses($m
)
Return Per. (yrs)
Ohio Valley Hypoth. Port All SCS, GU,
AEP
AIR EQE RMS
27
Hypothetical Portfolio
EP curves
o General
The EP curves and AALs tell the
same story. The maps show the different
distributions of AAL
But the underlying assumptions aredriving the results:
Smoothing, granularity, numberof events, etc.
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A big Thank You to
Participating Model Vendors
Modeling Teams and Presenters
As well as
The Steering Committee & QC Team
Special Recognition
Mark BoveMapping
Andrew MooreTechnical