1 tax exempt pre-event catastrophe reserves - in the wind? factor development david fennell casualty...
DESCRIPTION
3 s New York Dept of Ins factors: t Premium multipliers by state t Based on PCS data from 1950 to 1996 t Trended for Construction Cost Indices and Population Growth s Annual Reserve increment $2 Billion Origins of State Specific FactorsTRANSCRIPT
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TAX EXEMPT PRE-EVENT TAX EXEMPT PRE-EVENT CATASTROPHE RESERVES -CATASTROPHE RESERVES -
IN THE WIND? IN THE WIND?Factor Development
David FennellCasualty Loss Reserve SeminarSeptember 13, 1999
2
Origins of state specific factors Factors by line of insurance Modifying factors based on judgement Advantages and limitations recognized in
the analytical approach
OutlineOutline
3
New York Dept of Ins factors: Premium multipliers by state Based on PCS data from 1950 to 1996 Trended for Construction Cost Indices and
Population Growth Annual Reserve increment $2 Billion
Origins of State Specific Origins of State Specific FactorsFactors
4
Upcoming Year Cat Reserve47,000,000 to 385,000,000 (10)29,000,000 to 47,000,000 (9)16,000,000 to 29,000,000 (10)6,000,000 to 16,000,000 (9)
0 to 6,000,000 (13)
Annual Cat Reserve Annual Cat Reserve Increments Based on New Increments Based on New
York FactorsYork Factors
5
Historical cat data not collected by line of insurance
$2 Billion funds some but not all cats $3.2 Bil annual average cost since 1950 $4.2 Bil annual average cost since 1967
Line Of Insurance IssuesLine Of Insurance Issues
6
South CarolinaSouth Carolina
South Carolina - Allied Lines Losses
0
2
4
6
8
10
12
14
16
18
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Year
Indu
syrt
Loss
Rat
io
Excess
Normal
Used A.M. Best data by state and line of insurance
Available from 1967Use statistical properties to separate catastrophic losses from typical losses
Cat loss is loss in excess of mean plus X * Std dev
LINE OF INSURANCE ISSUESLINE OF INSURANCE ISSUES
7
South DakotaSouth Dakota
South Dakota - Allied Lines Losses
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Year
Indu
stry
Los
s Ra
tio
Excess
Normal
For states with less severe catastrophic history, the method could not work as well
LINE OF INSURANCE ISSUESLINE OF INSURANCE ISSUES
Low credibility for some state/line of insurance combinations Some data cleansing necessary
8
Factors based only on historical averages may be high or low depending on recent history of major cats in a state
South Carolina hurricane New Madrid earthquake
Probabilistic modeling provided an alternative which could inform judgement
Multiple vendors solicited for modeling indications Team analyzed six sets of modeling indications to supplement historical losses
Judgement ModificationsJudgement Modifications
9
Some modeling indications were for commercial versus personal lines risks.
Line of insurance had to be inferred afterward.
Models treat different perils differently Tornado/hail separate from hurricane
Some modelers did not include indications for all states Varying composition of model output
Issues With Judgement Issues With Judgement ModificationsModifications
10
ScatterScatter
Residential Hurricane/Tornado/HailScatter Plot
ALTERNATIVE X
NAIC FACTOR
AK
AL
ARAZ CA CO
CTDC
DE
FL
GA
HI
IAID IL IN KSKY
LA
MAMDMEMI MN MO
MS
MT
NC
NDNENHNJ
NMNVNY
OHOK
ORPA
RI SC
SDTNTX
UTVA
VTWAWIWV WY
The tool used for comparing methods was the scatter chart.
11
Regression LineRegression Line
Residential Hurricane/Tornado/HailScatter Plot
ALTERNATIVE X
ALTERNATIVE Y
AL
ARAZCACO
CTDC
DE
FL
GA
HI
IAIDIL
INKSKY
LA
MAMDMEMIMNMO
MS
MT
NC
NDNENHNJ
NMNVNY
OHOK
ORPA
RI SC
SDTNTX
UTVA
VTWAWIWVWY
The agreement between methods was measured by the regression line.
12
Residual PlotResidual Plot
Residential Hurricane/Tornado/HailScatter Plot
Stud. Residuals
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
WV
WY
AKALAR
AZCA
CO
CTDCDE
FLGA
HI
IAID
ILIN
KS
KYLAMAMDMEMIMN
MOMS
MTNC
NDNE
NHNJ
NMNV
NYOH
OK
ORPARI
SCSD
TN
TX
UTVAVTWAWIWV
W
Residual plots from the regression detected outliers
13
Weighted RegressionWeighted Regression
Commercial Hurricane/Tornado/HailWeighted Regression
WTD AVG
MODELERS
NAIC FACTOR
AK
AL
ARAZ CA
COCT
DCDE
FL
GA
HI
IAID
ILIN
KSKY
LA
MAMDMEMI MNMO
MS
MT
NC
NDNENHNJ
NM NVNY
OH
OK
ORPA
RI
SC
SDTN
TX
UTVA
VTWAWIWV WY
Weights for each method were derived by team consensus based on perceived similarity of the alternative to our reserve approach.
14
OutliersOutliers
Commercial Hurricane/Tornado/HailWeighted Regression
Stud. Residuals
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
WV
WY
AKAL
ARAZ
CACO
CTDC
DE
FL
GA
HI
IAID
ILINKSKY
LAMAMDMEMI
MNMOMS
MTNC
NDNE
NHNJ
NM
NV
NYOH
OK
ORPA
RISCSD
TN
TX
UTVAVTWAWIWV
W
States more than than two standard deviations from zero were considered candidates for modification.
15
Homeowners factors modified by judgement for CO, HI, KS, NE, OK
CMP factors modified for FL, HI, NV Allied Lines factors modified for AL, DE, FL, HI
Wind Factors ModifiedWind Factors Modified
16
Considerations for modification given to 12 states: AK, AR, IL, IN, KY, MO, MS, OH, SC, TN, UT, WA
Earthquake history for these 12 states was lacking Regression approach had to be adapted due to lack
of ability to fit a meaningful regression line Assumed that California had credible history and
forced regression line through it
Earthquake Factors ModifiedEarthquake Factors Modified
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Factors were built considering Exposure: Cost and population indices Funding: Best allocation of a fixed reserve increment
amount Frequency: Both modeled and historical Severity: Historical excess losses and model
simulations Credibility: Adjustments made in some premium line
of insurance combinations Data quality: Investigations uncovering data anomalies
led to some factor revisions
ConclusionsConclusions