capital allocation for property-casualty insurers: a catastrophe reinsurance application cas...
DESCRIPTION
3 Economic Role of Capital in Insurance 4 Affects value of default when insolvency occurs 4 Default = expected policyholder deficit (market value) 4 More capital implies smaller default value (good) 4 But more capital implies higher capital cost (bad) 4 Equilibrium: Capital Cost Solvency Benefit Capital AmountTRANSCRIPT
Capital Allocation for Property-Casualty Insurers: A Catastrophe Reinsurance
Application
CAS Reinsurance SeminarJune 6-8, 1999
Robert P. ButsicFireman’s Fund Insurance
2
Yes, Capital Can Be Allocated!
Outline of Presentation: General approach: Myers-Read model
– Joint cost allocation is a common economics problem– Another options-pricing application to insurance– Extensions, simplification and practical application of MR method
Reinsurance (and primary insurance) application: the layer as a policy
Semi-realistic catastrophe reinsurance example Results and conclusions
3
Economic Role of Capital in Insurance
Affects value of default when insolvency occurs Default = expected policyholder deficit (market value) More capital implies smaller default value (good) But more capital implies higher capital cost (bad) Equilibrium:
Capital Cost
Solvency Benefit
Capital Amount
4
Fair Premium Model
For all an insurer’s policies:
Important Points:– Shows cost and benefit of capital– All quantities at market values (loss includes risk load)– Loss can be attributed to policy/line– But C and D are joint
Single policy model :
CTDLP
iiii CTDLP
5
Allocation Economics
Capital ratios to losses are constant:
Premiums are homogeneous:
Implies that
And marginal shift in line mix doesn’t change default ratio:
Solve this equation for
iiLcC
iiii LPLP
dLDLDd iii
dLD i
ic
6
Lognormal Model
To solve for we need to specify relationship between L, C and D
Assume that loss and asset values are lognormal D is determined from Black-Scholes model Final result (modified Myers-Read):
ic
)1()1()(
)()1(2
2
AL
ALiA
L
LiLi yN
ynccc
7
Simplifying the Myers-Read Result
Assume that loss-asset correlation is small Define Loss Beta:
Result:
Implications:– Relevant risk measure for capital allocation is loss beta– Capital allocation is exact; no overlap– Allocated capital can be negative– Z value is generic for all lines
2/ LiLi
Zcc ii )1(
8
Numerical Example
Table 1.1
Loss Beta and Capital Allocation for Numerical Example
Liability Loss Loss Capital/
Value CV Beta Liability Capital
Line 1 500 0.2000 0.8463 0.3957 197.87Line 2 400 0.3000 1.3029 0.7055 282.19
Line 3 100 0.5000 0.5568 0.1993 19.93
Total 1000 0.2119 1.0000 0.5000 500.00
9
Negative Capital Example
Original Add Low- AdjustCase Risk Policy Capital
Number of PoliciesPolicy 1 1000 1000 1000Policy 2 0 1 1
Loss CVPolicy 1 10.0 10.0 10.0Policy 2 1.0 1.0Total 0.3162 0.3159 0.3159
Expected Loss 1000.000 1001.000 1001.000Capital 500.000 500.000 499.660Default 16.594 16.586 16.610
Default Ratio 1.6594% 1.6569% 1.6594%
Capital/Loss, Policy 2 -0.340
Assumptions:
• losses are independent
•no asset risk
•total losses are lognormal
10
Reinsurance Application
For policy/treaty, capital allocation to layer depends on:– covariance of layer with that of unlimited loss– covariance of unlimited loss with other risks
Layer Beta is analogous to loss beta Capital ratio for policy/layer within line/policy:
Point beta for layer is limit for narrow layer width:
1
)()(1)(
0
12 xEX
xEs
x
Zcc kikkik
iki
1
11
Point Betas for Some Loss Distributions
0.00
5.00
10.00
15.00
20.00
25.00
0 50 100 150 200 250 300 350 400
x
Legend: right hand side (x = 400), top to bottomParetoLognormalExponentialGammaNormal
12
Market Values and Risk Loads
Layer Betas depend on market values of losses Market values depend on risk loads Modern financial view of risk loads
– Adjust probability of event so that investor is indifferent to the expected outcome or the actual random outcome
– Risk-neutral valuation– General formula:
In finance, standard risk process is GBM lognormal– Risk load equals location parameter shift:
0 )(ˆ)1(ˆ dxxfxXX
)exp()exp(1
13
Reinsurance Risk Loads
Risk-neutral valuation insures value additivity of layers Risk load for a layer
– integrate R-N density instead of actual density, giving pure premium loaded for risk
– risk load is difference from unloaded pure premium
Point risk load– load for infinitesimally small layer– parallel concept to point beta
Simple formula:
.1)()(ˆ
)( xGxGx
14
General Layer Risk Load Properties
Monotonic increasing with layer Generally unbounded Zero risk load at lowest point layer Lognormal example:
location PS
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
0 200 400 600 800 1000
x
15
PRL and the Generalized PH Transform
Location parameter shift may not be “risky” enough Wang’s Proportional Hazard transform
More general form:
Gives all possible positive point risk loads Fractional transform:
– No economic basis– But it works
qxGxG )]([)(ˆ 10 q
)()]([)(ˆ xqxGxG
mxqxxq
)(
16
Parameter Estimation
Market valuation requires modified statutory data Representative insurer concept necessary for capital
requirements– particular insurer could have too much/little capital, risk, line mix, etc.– industry averages can be biased
Overall capital ratio CV estimates
– losses: reserves and incurred losses, cat losses– assets
Catastrophe beta
17
Catastrophe Pricing Application
Difficult, since high layers significantly increase estimation error But, made easier because cat losses are virtually independent of
other losses Present value pricing model has 3 parts:
– PV of expected loss:
– PV of risk load:
– PV of capital cost:
)1/(),( rbaX
)1/(),(),( rbabaX
)1)(1(),()],(1)[,(
trrtbacbabaX
18
Example: Annual Aggregate Treaty
a bExpected
LossCapital
CostRiskLoad
FairPremium
ExpectedLoss Ratio
ImpliedROE
0 100 31.51 0.74 8.78 41.04 0.814 0.272100 200 6.83 0.73 4.73 12.29 0.589 0.213200 300 3.01 0.58 2.64 6.24 0.512 0.180300 400 1.66 0.46 1.67 3.79 0.463 0.161400 500 1.03 0.38 1.14 2.55 0.427 0.149500 600 0.69 0.32 0.82 1.83 0.398 0.140600 700 0.48 0.27 0.62 1.37 0.374 0.133700 800 0.35 0.23 0.47 1.06 0.354 0.127800 900 0.27 0.20 0.37 0.84 0.336 0.123900 1000 0.21 0.18 0.30 0.69 0.321 0.119
1000 Infinity 1.13 2.33 2.03 5.48 0.219 0.094
0 Infinity 47.17 6.42 23.58 77.18 0.648 0.137
19
Return on Equity for Treaty
Look at point ROE Varies by layer Equals risk-free interest rate at zero loss size
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0 20 40 60 80 100
20
Summary
How to allocate capital to line, policy or layer– Key intuition is to keep a constant default ratio– Relevant risk measure is loss or layer beta– Allocated capital is additive
Reinsurance and layer results– Layer betas are monotonic, zero to extremely high– Layer risk loads are monotonic, zero to extremely high
ROE pricing method has severe limitations– ROE at fair price will vary by line and layer– capital requirement can be negative
21
Conclusion
Capital allocation is essential to an ROE pricing model– capital is the denominator– but this model has severe problems
It’s less (but still) important in a present value pricing model
– capital determines the cost of double taxation– this model works pretty well (cat treaty example)
The real action is in understanding the risk load process – knowing the capital requirement doesn’t give the price– because the required ROE is not constant
We’ve got a lot of work to do!