Why Pooling Works - CAJPAWhy Pooling Works CAJPA Spring 2017 Mujtaba Datoo Actuarial Practice...
Transcript of Why Pooling Works - CAJPAWhy Pooling Works CAJPA Spring 2017 Mujtaba Datoo Actuarial Practice...
Why Pooling Works
CAJPA Spring 2017
Mujtaba DatooActuarial Practice Leader, Public EntitiesAon Global Risk Consulting
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Discussion Points
Mathematical preliminaries Why insurance works Pooling examples Loss distributions Simulation Extreme events: low frequency, high severity To pool or not to pool…
Mathematical and StatisticalPreliminaries
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Trivia…Statistics Defined
Latin origin: statisticum collegium meaning “council of state”
Italian connection: statista meaning “statesman”
German context: statistik meant analysis of data about the state
English term: “political arithmetic”
After 19th Century, “statistics” was introduced to refer to any collection and classification of data
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Trivia…Mathematics Defined
Pythagorean origin “That which is learned”
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Definitions
Risk: Deviation or variability around an expected outcome– Uncertainty in timing and amount of payment
Liquidity: Ability to pay in cash short term liabilities, usually in one year Solvency: Ability to pay all liabilities in the long run
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Law of Large Numbers
Sample mean converges to population mean as sample size increases Variance of means
–
– As n gets larger, variance is minimized– Coefficient of Variation (CV)
• Ratio of the standard deviation to the mean• Normalizes the scale• Compare entities (not distorted by volume or size)
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Law of Large NumbersCV decreases with increasing sample size
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Central Limit Theorem
For data from any distribution, if we repeatedly take n independent random samples– As “n” becomes large
• The distribution of the sample means will approach a normal distribution.
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Common Measures of Risk
Measure dispersion:– Variance, standard deviation, average absolute deviation, Value at
Risk (VaR), Tail Value at Risk (TVaR), Coefficient of Variation (CV) Other measures:
– Mean (aka average, expected), median, mode
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Key Measure: Coefficient of Variation
Coefficient of Variation (CV) =
The smaller the CV, the smaller the variability around the average
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Random Number Generation
Used for simulation To “mimic” a distribution where standard functions cannot be
“‘accurately” fit Can generate random numbers in Excel
– RAND() – generates numbers between 0 and 1– RANDBEWTEEN(low, high) – generates whole numbers between
selected low and high number
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Generating 1,000 Random Numbers Between 0 and 11 0.6152 0.7903 0.8794 0.3315 0.1636 0.9527 0.0678 0.0609 0.934
10 0.12511 0.64412 0.99213 0.66614 0.71215 0.70516 0.93417 0.232 Bin Freq Cum % Inc %18 0.684 0.0 0.0 0 0.0%19 0.730 0.1 0.1 99 9.9% 9.9%20 0.544 0.2 0.2 107 20.6% 10.7%21 0.664 0.3 0.3 93 29.9% 9.3%22 0.080 0.4 0.4 81 38.0% 8.1%23 0.901 0.5 0.5 112 49.2% 11.2%24 0.176 0.6 0.6 98 59.0% 9.8%25 0.484 0.7 0.7 107 69.7% 10.7%26 0.613 0.8 0.8 96 79.3% 9.6%27 0.271 0.9 0.9 104 89.7% 10.4%28 0.404 1.0 1.0 103 100.0% 10.3%29 0.00530 0.29831 0.59632 0.12233 0.29334 0.88235 0.21636 0.91437 0.11938 0.28739 0.19940 0.28641 0.30142 0.032
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Frequ
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Bin
Histogram
Freq Cum %
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Law of Large NumbersCoin toss example
Coin toss Probability (heads) = 0.5 As more coins tossed,
Pr(H) 0.5 (p=1/2) e.g. random 10 tosses
produces Pr(H) = 0.7,however as number of tosses increase Pr(H) will converge to 0.5
Trial#
Result H = 1T = o
Sum of Heads
Pr(H)(4)/(1)
(1) (2) (3) (4) (5)
1 H 1 1 1.00
2 T 0 1 0.50
3 T 0 1 0.33
4 H 1 2 0.50
5 H 1 3 0.60
6 H 1 4 0.67
7 H 1 5 0.71
8 T 0 5 0.63
9 H 1 6 0.67
10 H 1 7 0.70
….
1000 H 1 506 0.51
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Simulated Coin Toss
Trial # Rand() ResultH=1, T=0
Sum of H Pr(H)
1 0.68 T 0 0 0%2 0.34 H 1 1 50%3 0.95 T 0 1 33%4 0.58 T 0 1 25%5 0.66 T 0 1 20%6 0.18 H 1 2 33%7 0.95 T 0 2 29%8 0.96 T 0 2 25%9 0.59 T 0 2 22%
10 0.68 T 0 2 20%11 0.56 T 0 2 18%12 0.07 H 1 3 25%13 0.82 T 0 3 23%14 0.24 H 1 4 29%15 0.39 H 1 5 33%16 0.86 T 0 5 31%17 0.90 T 0 5 29%18 0.62 T 0 5 28%19 0.15 H 1 6 32%20 0.39 H 1 7 35%
0%10%20%30%40%50%60%70%80%90%
100%
1 101 201 301 401 501 601 701 801 901Trial #
Pr(H)
Why Insurance Works
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Why Insurance Works
Example from Society of Actuary paper “Risk and Insurance” The example is about the loss amount of a car owner for a certain year
Loss Amount ProbabilityNumber of Policyholders
1 10 1,000 1,000,000
$0 80% $0 $0 $0500 10% 50 500 50,000
5,000 8% 400 4,000 400,00015,000 2% 300 3,000 300,000
Average Loss per Policyholder
$750 $750 $750
Average Total Losses (μ)
750 7,500 750,000
Variance (σ ) ≈ 6 million ≈ 60 million ≈ 6 billionStandard
Deviation(σ)2,442 7,722 77,217
Coefficient of Variation (CV)
3.26 1.03 0.10 ≈ 0
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Why Insurance Works
People seek security, insurance spreads the risk and can provide the security
To measure the potential variability of the loss (Risk), we use the standard deviation.
The existence of the insurance industry does not decrease the frequency or severity of loss.
Policyholders are willing to pay a gross premium for an insurance contract, which exceeds the expected value of their loss, in order to substitute the fixed low variance premium payment for an unmanageable amount of risk inherent in not insuring
Pooling Example
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Scatter Diagram – Below $100,000
Single Entity Pool
$0
$25,000
$50,000
$75,000
$100,000
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Scatter Diagram – Above $100,000
Single Entity Pool
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
$800,000
$900,000
$1,000,000
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$200,000
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$1,000,000
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Scatter Diagram – Combined
Below $100,000 Above $100,000
$0
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$100,000
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Single Entity Pool
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
$800,000
$900,000
$1,000,000
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Single Entity Pool
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Loss Rates: Pool vs Single Entities
$0.00
$1.00
$2.00
$3.00
$4.00
$5.00
$6.00
$7.00
$8.0020
07
2008
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PoolABCDEFGH
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Risk MeasuresPool has lower CV than individual entities
Std. Dev. Average CV
POOL 0.3 $2.03 0.17
A 0.3 $0.97 0.35
B 0.5 $2.47 0.21
C 0.9 $1.50 0.58
D 1.3 $1.62 0.80
E 1.6 $2.19 0.71
F 2.1 $3.72 0.57
G 2.6 $1.73 1.51
H 0.8 $2.11 0.40
0.17
0.35
0.21
0.58
0.80 0.71
0.57
1.51
0.40
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1.00
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1.60
Poo
l A B C D E F G H
CV
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Funding: Pool vs Single Entity
Single Entity is relatively volatile, therefore– Need more capital to absorb greater variability– Potential overfunding or underfunding in any given year is more
likely
Building a Workers CompensationProbability Distribution
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WC Empirical Distribution
payroll $100,000,000 frqunce per $1M 1expected number of claims 100
avg annual salary $50,000 number of workers 2,000 Prob of claim per worker 5%prob of NO claims = 95%
Med only % of claims 70%TD % of claims 25%PD% of claims 5%
severity average range - can fit to a loss ditributionMed Only $1,000 50 to 1,500 TD $20,000 10,000 to 30,000 PD $200,000 100,000 to 300,000
Probabilties random number rangeNO claim 95.00% 0.9500 0.0001 to 0.9500Med only 3.50% 0.9850 0.9500 to 0.9850TD 1.25% 0.9975 0.9850 to 0.9975PD 0.25% 1.0000 0.9975 to 1.0000
Loss Distribution Curve Fitting
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Fit a Curve
Empirical histogram, (from scatter diagram) Fit data to “standard curves”
– Estimate parameters from data– e.g. Normal (bell-shaped symmetrical curve)– e.g. skewed distributions: Weibull, lognormal, Burr, Gamma, Pareto
Simulation– Ensure it “mirrors” the (empirical) data
Allows interpolating or extrapolating beyond observed experience– e.g. if no large claims emerged, but can extrapolate from the fitted
curve
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Distribution of Incurred Losses
$0
$100,000
$200,000
$300,000
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Claim Data Adjustments
Check for reasonability Detect outliers Develop – bring claims to ultimate value Trend – bring to a common date, like CPI
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Data Morphing
Historical data may not reflect future changes– e.g. medical technological improvement such as nano-surgery
• Impacts future medical costs– Tort threshold changes, e.g. North Dakota– Judicial impact – e.g. Florida Castellanos, et al.
Increase in life expectancy, e.g. impact on PD claims Economic disturbances, structural transformation (technology impact)
Make judgmental adjustments
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Stratified Losses – HistogramUsed to fit a curve
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Fit the “Best” Curve: Lognormal, Weibull, Gamma
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‒ Lognormal‒ Weibull‒ Gamma
Excess Pool Loss Distribution Example
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Distribution of Losses (Developed, Trended)Losses> $25,000 – about 1,300 over 10 years
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Methodology, Stratify ClaimsAbove $100,000
Project losses above $100,000:– Use last 10 years of data– Sparse data– Volatile, less predictable– Use modeling:
• Pareto distribution
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Methodology: Claims Above $100,000Frequency x Severity
Frequency– Number of claims per
exposure unit– Adjust historical data to a
common year, say 2016/17– Select from 10-year history
Severity– Average cost per claim– Adjust historical data to a
common year, say 2016/17– Fit a Pareto statistical
distribution to the underlying data
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10-year history of claimsDeveloped and trended
$0
$1
$2
$3
$4
$5
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ions
Over 10 Years, 600 claims over $100K250 claims over $250K100 claims over $500K40 claims over $1M15 claims over $2M6 claims over $3M5 claims over $4M3 claims over $5M
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Pareto Statistical Distribution
Used in actuarial literature to estimate large claims Calculate within each layer, say $1,000,000 to $2,000,000:
– % of claims– Average cost per claim
Has “fat tail” – allows for very large claims to be considered
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Stratify Claims Over $100,000Fit to a “theoretical” curve
ClaimsAbove
Threshold
Numberof
Claims
Ratio to $100,000Claims
ParetoFit to
$100,000$100 K 600 100% 100%
$250 K 250 42% 38%
$500 K 100 17% 19%
$1 M 40 7% 9%
$2 M 15 3% 4%
$3 M 6 1% 3%
$4 M 5 0.8% 2.1%
$5 M 3 0.5% 1.7%
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FrequencyClaims Above $100,000
Calculate number of claims per exposure unit
Review for reasonability Select frequency, say for 2016/17
= 0.0375 claims for every $1 million of payroll
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Projected 2016/17 Claims Over $100,000From Pareto Fit
Layer Payroll Frequency
% ofClaimsin Layer
ProjectedNumber of
Claims
$1 - $2 M $1.5 B 0.0375 9% 5.0
$2 - $3 M $1.5 B 0.0375 4% 2.5
$3 - $4 M $1.5 B 0.0375 3% 1.6
$4 - $5 M $1.5 B 0.0375 2% 1.2
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Projected 2016/17 Severity Over $100,000From Pareto Fit
Layer StraightAverage
ParetoFit
$1 - $2 M $1,500,000 $1,683,067
$2 - $3 M 2,500,000 2,804,004
$3 - $4 M 3,500,000 3,857,807
$4 - $5 M 4,500,000 4,888,368
• Pareto fit reflects skewed nature of large claims
• Of the 600 claims above $100,000, about 9% will exceed $1 million
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Projected Losses for 2016/17From Pareto Fit
LayerProjectedNumber of
ClaimsSeverity Projected
Losses
$1 - $2 M 5.0 $683,067 $3.4 M
$2 - $3 M 2.5 804,004 2.0 M
$3 - $4 M 1.6 857,807 1.4 M
$4 - $5 M 1.2 888,368 1.1 M
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Size of Loss Distribution
Layer
Total Reported Claims
Percent of Total
Cumulative Percent of
Total
Total Trended Developed
LossesPercent of Total
Cumulative Percent of
Total$25,000 to $500,000 1,159 89.8% 89.8% $139,694,990 49.1% 49.1%
$500,000 to $1M 91 7.1% 96.9% 62,628,246 22.0% 71.1%
$1M to 2M 27 2.1% 99.0% 33,370,164 11.7% 82.9%
$2M to 3M 7 0.5% 99.5% 16,252,543 5.7% 88.6%
$3M to 4M 1 0.1% 99.6% 3,639,981 1.3% 89.9%
$4M to 5M 2 0.2% 99.8% 9,029,413 3.2% 93.0%
$5M to 10M 3 0.2% 100.0% 19,795,046 7.0% 100.0%
Over $10M 0 0.0% 100.0% 0 0.0% 100.0%
Total 1,290 100% $284,410,384 100%
Simulation Demonstration
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Why Simulate?
Simulated results generate a distribution of aggregate losses (frequency times severity)
From this aggregate distribution, can derive:– Average (expected value)– Percentiles
• Rank results in increasing order• Divide (or mark off) various percentiles, e.g. 70th, 90th
• Calculate VaR, TVaR Adjust for various limits (SIR), aggregate losses, deductibles, etc.
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Monte Carlo Simulation
For each claim, select sizeSelect number of claims
Use claim stratification information to simulate claims process
Number of claims (frequency)
Average claim size (severity)
$1,000 $2,000 $3,000
$4,000 $5,000
$2,000
$6,000
$5,000
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Monte Carlo SimulationNumber of Claims
0%
5%
10%
15%
20%
1 2 3 4 5 6
% o
f Obs
erva
tions
Number of Claims
Uniform Distribution,i.e. equally likely to occur
Number of Claims
Poisson Distribution,Mean = 100
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Monte Carlo SimulationClaim Size
0%
5%
10%
15%
20%
25%
30%
35%
1,000 2,000 3,000 4,000 5,000 6,000
% o
f Cla
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Claim Size
Lego Distribution
100 500 2,500 10,000 50,000 100,000250,000Claim Size
Lognormal Distribution
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Monte Carlo SimulationExample – Simulated Claims
TrialNumber
Numberof Claims
Claim Amount for Claim #Aggregate
Losses1 2 3 4 5 61
2
3
4
5
…
9,999
10,000
Average
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Monte Carlo SimulationExample – Simulated Claims
TrialNumber
Numberof Claims
Claim Amount for Claim #Aggregate
Losses1 2 3 4 5 61 2 $2,000 $4,000 $6,000
2 4 5,000 3,000 4,000 3,000 15,000
3 2 4,000 3,000 7,000
4 5 1,000 6,000 4,000 1,000 3,000 15,000
5 4 6,000 4,000 4,000 2,000 16,000
…
9,999 3 4,000 4,000 4,000 12,000
10,000 5 2,000 3,000 3,000 4,000 3,000 15,000
Average 3.5 $12,000
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Monte Carlo SimulationClaim Size Comparison
0%
5%
10%
15%
20%
25%
30%
35%
1,000 2,000 3,000 4,000 5,000 6,000
% o
f Cla
ims
Claim Size
Lego Distribution
1,000 2,000 3,000 4,000 5,000 6,000Claim Size
Simulation Distribution
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Monte Carlo SimulationExample – Ranked Simulated Claims
Trial Number
Aggregate Losses
1 $5,348,825
2 4,884,701
3 5,925,967
4 3,551,488
5 4,166,714
6 4,647,452
7 2,522,083
…
9,999 3,825,756
10,000 5,353,735
Average $5,571,671
RankAggregate
Losses
1 $2,282,852
2 2,393,690
3 2,522,083
…
5,000 5,310,869
…
7,000 6,117,601
…
9,000 7,615,029
10,000 27,533,442
Average $5,571,671
PercentileConfidence Level Factor
70% 1.10
90% 1.37
$6.1M / $5.6M =
$7.6M / $5.6M =
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Monte Carlo SimulationExample – Confidence Level
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
3.3 3.5 3.8 4.1 4.5 4.8 5.1 5.6 5.8 6.1 6.7 7.6 8.5 11.4
% o
f Tria
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Aggregate Losses ($Million)
70% confidence
level
Expected level
99.5%1 in 200 year
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Model RiskOne in a 100-year event
0
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1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
169,396 games played215 no-hitters19 perfect games
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Model RiskParameters shift with time
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1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Frequency first 80 years 0.00012%
5 timesFrequency next 20 years 0.00066%
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Risk Margin
An amount that recognizes uncertainty VaR (say at 90%) minus E(X) = risk margin Other measures to set risk margin
– Financial ratios– Risk-based capital– Solvency II, ORSA
Several considerations in setting risk margin– Solvency– Catastrophic events– Ratings– Etc.
Extreme EventsLow Frequency, High Severity
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Extreme Events
The black swan event Cannot quantify readily extreme events in tails – need for complex
statistical approaches (copulas) and simulations– Be wary of its limitations– Isolated or correlated extreme events is where “upper” bound of
surplus can be targeted Understand well and use judgment to evaluate
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Extreme EventPictorially
Assets = Reserves + Surplus
Median Expected Claim Costs50th Percentile
Expected Policyholder Deficit of 1% to 2%
40%30%20%10%
Black swan lives here!
50% 90%
Pro
babi
lity
of
Cla
im C
osts
RBC
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VaR and TVaR
Value-at-Risk (VaR)– Threshold value that losses to a certain confidence level, say 95%
of cases would not be exceeded– Solvency II calibrates 99.5% over a one-year horizon
Tail Value-at-Risk (TVaR)– Takes the average of all the values in the tail above VaR threshold
for a specific time period– Average loss amount of “extreme” events– Actual experience may not exist– Mostly estimated by simulation
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VaR and TVaRSimulation Example
Trial Number
Aggregate Losses
1 $5,348,825
2 4,884,701
3 5,925,967
4 3,551,488
5 4,166,714
6 4,647,452
7 2,522,083
…
9,999 3,825,756
10,000 5,353,735
Average $5,571,671
RankAggregate
Losses
1 $2,282,852
2 2,393,690
3 2,522,083
…
5,000 5,310,869
…
9,500 8,464,392
9,501 8,473,869
…
10,000 27,533,442
Average $5,571,671
VaR at 95% = $8,464,392
Average = $9,794,388= TVaR at 95%
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Asset Liability Matching
Duration of investments– Investment policy– Restrictions, conservative (usually bonds)
Payout pattern Large claim
– WC– Liability
Liquidate bond (asset)– Pricing loss (risk)
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The Formula That Killed Wall Street
By Felix Salmom WIRED MAGAZINE 02.23.2009
To Pool or Not to Pool...That is the Question
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Minimizing TCOR
Expected losses + expenses (overhead, reins, claims admin) + risk margin
Most (public entities) seek stability for budgeting – Stability is key– Smooth out large random, volatile claims– If not stable, need source of funding
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Types of Risk
Process risk:– Associated with projection of future contingencies that are
inherently variable Parameter risk:
– Associated with selection of parameters of the model (e.g., selecting inapplicable LDF)
Model risk: – Misidentifying a process model (e.g., Poisson for frequency)
Surplus provides protection against variation
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Risk CategoriesPublic entities: mostly underwriting risk
Investment risk Credit risk
– Reinsurance recoverable– Other credit risks
Underwriting risk– Premium (pricing) risk– Loss reserve risk
Operating risk Catastrophe risk
– Floods– Earthquake
premium written 32%
credit 10%
loss reserves
27%
investm't31%
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Balance Sheet
02468
101214161820
Assets Liabilities
Bonds
Stocks
Cash
Receivables
RealEstate
Case Reserves
IBNR
ULAEOther
Surplus
$Mill
ions
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Variability
Pooling in itself does NOT reduce frequency or severity Reduces variability
– Yields stability Higher SIRs imply
– greater volatility– more liquidity needed– More surplus needed
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Pooling: Long Term Stability
Pooling – Produces more stable long term averages
Individual entity– Volatile– Not conducive to budgeting
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Pooling
Advantages
Stability of rates Economies of scale
– Services– Loss control– Litigation management
Leveraging expertise Purchasing power Equity belongs to members Adds homogeneous risks to pooling,
increases volume (credibility) of data
Concerns (from individual entity’s perspective) Assessable Joint & Several liability “Sharing” – allocation equity Diversification
– Geographical– Homogeneity of risks
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Ken Hearnsberger, thanks
Master practitioner Suggested the subject matter
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Questions?
Mujtaba Datoo, ACAS, MAAA, FCAActuarial Practice LeaderAon Global Risk Consulting(949) [email protected]
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Thank You!
Pyx Chamber,
Westminster Abbey
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The name “Pyx” refers to small boxes, containing the official samples of gold and silver coinage which were also kept here. New coins were annually tested against these samples in a public “Trial of the Pyx,” held in the Palace of Westminster.
1859 engraving, showing the Pyx Chamber still containing cupboards for state documents.
Thank You!