CAS Fair Value Project
Casualty Actuaries in Europe
Spring Meeting
23 April 2004
E. Daniel Thomas
(1) 646-471-5746
edward.d.thomas@us.pwc.com
PwC
Disclaimer
• The opinions expressed should not be regarded as representing
the views of the CAS or of PricewaterhouseCoopers LLP
2
Presentation Outline
• CAS Project Objectives
• Data Characteristics
• Modeling Approaches
• Preliminary Findings
• Significant Issues
3
CAS Project Objectives
• Objectives
– Evaluate impact of fair value on U.S. insurance company
loss and LAE reserves
– Identify significant issues associated with the usefulness of
fair values in insurance company financial statements
• Issues outside of scope
– Credit risk
– Adequacy of booked reserves
– Correlation adjustments across lines of business
– Impact of fair value on other balance sheet items
4
Data Characteristics (1)
• Loss Data
– 1998 - 2002 Schedule P
– 10 accident years of development
– Net of reinsurance basis
– U.S. Statutory basis (sufficiently close to U.S. GAAP)
• Interest rates
– 31 Dec 1998 - 2002
– U.S. Treasury Securities
– Maturities range from 6 months to 30 years
5
Data Characteristics (2)
• Three lines of business
– Personal Auto Liability
– Workers’ Compensation
– Medical Malpractice Claims-Made
• PwC selected ten companies for each line of business (2 large, 2
medium, 3 small, 3 multi-line)
6
Modeling Approaches
• FV Reserves = (U.S. GAAP) – (Discount) + (Market Value Margin)
= (U.S. GAAP) x (1 – Discount Factor) x (1 + MVM)
• Discount Factor Models
– Duration
– Matched to yield curve
• MVM Models
– Development model – standard deviation
– Stochastic simulation – standard deviation
– Stochastic simulation – percentile distribution
– Return on Capital
• Model Calibration
7
Findings – Discount Modeling
• Discount Models
– Well-defined approaches available
– Generally no significant differences between duration and
matching approaches
– Interest rate fluctuations affect results
– Shape of yield curve can affect results
8
Impact of Discount Factor Models
Duration vs. Matched to Yield-Curve
Sum of Total Discount
Line
Personal Auto Liability
Year
Co code
D (L)
E (L)
C (M)
F (M)
G (M)
A (S)
B (S)
H (S)
I (S)
V (S)
9
Interest Method
Duration
Matched
Duration
Matched
Duration
Matched
Duration
Matched
Duration
Matched
Duration
Matched
Duration
Matched
Duration
Matched
Duration
Matched
Duration
Matched
1998
8.0%
8.0%
5.8%
5.8%
8.2%
8.2%
7.2%
7.2%
6.7%
6.7%
7.5%
7.5%
8.2%
8.2%
7.3%
7.3%
6.8%
6.8%
4.4%
4.4%
1999
10.4%
10.6%
7.7%
7.9%
10.0%
10.2%
9.3%
9.4%
9.3%
9.5%
7.5%
7.7%
10.0%
10.2%
9.6%
9.7%
7.7%
7.9%
5.6%
5.8%
2000
8.5%
8.4%
6.9%
6.7%
8.2%
8.0%
7.9%
7.8%
7.8%
7.7%
6.9%
6.7%
8.2%
8.1%
7.8%
7.6%
7.9%
7.8%
5.5%
5.3%
2001
4.5%
5.9%
4.4%
5.6%
4.3%
5.6%
4.4%
5.6%
3.9%
5.0%
4.5%
5.9%
4.6%
5.9%
4.9%
6.1%
3.7%
4.9%
2.5%
3.3%
2002
2.5%
3.5%
2.3%
3.2%
2.3%
3.2%
2.5%
3.4%
2.2%
3.0%
0.7%
0.7%
2.7%
3.8%
2.8%
3.8%
2.4%
3.3%
1.6%
2.1%
Findings – MVM Modeling
• MVM Models
– Many approaches, no single approach universally preferred
– For a given company and year-end, the MVM varied by
model, sometimes significantly
– For a given company and MVM model, the MVM varied over
the time period studied, sometimes significantly
– For a given company, the MVM by model did not always
move in parallel over the time period (i.e. one MVM model
did not always result in the highest MVM) adding further
uncertainty and variation in the results
– For smaller companies, the MVM tended to be larger
(measured as a percentage of the loss reserves)
10
MVM by Company –
Personal Auto Liability
MVM by Company at Year-End 2002
100%
90%
80%
MVM %
70%
60%
ROC MVM
50%
40%
30%
20%
10%
0%
0
2
4
6
Company
11
8
10
12
MVM by Company for One Method –
Personal Auto Liability
MVM by Company at Each Year-End (Development Model)
A (S)
120%
B (S)
H (S)
100%
MVM (%)
I (S)
80%
V (S)
C (M)
60%
F (M)
G (M)
40%
D (L)
20%
E (L)
Average
0%
1997
1998
1999
2000
Year-End
12
2001
2002
2003
MVM by Method for One Company –
Personal Auto Liability
MVM by Method - Company C(M)
18%
16%
14%
Develop SD
MVM %
12%
Stoch SD
10%
Percentile
ROC
8%
6%
4%
2%
0%
1998
1999
2000
Year-End
13
2001
2002
MVM by Company Size –
Personal Auto Liability
MVM for All Companies and All years
70%
60%
MVM (%)
50%
MVM vs.
Company
Size
40%
30%
20%
10%
0%
0
200,000
400,000
600,000
Reserves ($000)
14
800,000
1,000,000
Findings – Balance Sheet Impact
• Balance Sheet – Loss Reserves
– Personal auto liability - FV reserves were generally greater than
U.S. GAAP reserves
– Workers’ compensation - FV reserves were generally less than or
close to the U.S. GAAP reserves
– Medical malpractice claims-made - Not credible findings
– Impact of moving to fair value loss reserve tended to be greater
for smaller companies (i.e. higher MVM charge)
– Associated result that FV would have on assets and other
liability values would be needed to assess impact on equity
15
Findings – Income Statement Impact
• Income Statement – Incurred Losses
– FV current accident year incurred losses generally greater than
U.S. GAAP. This tends to defer recognition of profits.
– FV calendar year incurred losses from prior accident years may
not be benchmarked to zero (i.e. no impact on future U.S GAAP
calendar year results if prior year loss reserves were perfectly
estimated)
– Under FV, a non-zero impact on future calendar year results can
be generated without changing undiscounted loss reserve estimate
(depending on the relative amounts of discount and MVM)
– Leveraged impact of reserve changes on income statement
16
Significant Issues
• Modeling Issues
• MVM Estimation Issues
• Financial Statement Presentation Issues
17
Modeling Issues
• Payment patterns
– Incomplete historical paid triangles
– Data smoothing reversals, unusual data points
• Measures of variation
– Influenced by constraint to accept booked reserves as mean
of distribution
– Variation associated with the tail/prior accident year bucket
– Variation for certain liabilities not amenable to statistical
analysis (e.g. asbestos & environmental)
18
MVM Estimation Issues
• Single MVM guideline based on an industry guideline
unlikely to be appropriate
• Actuarial and/or accounting literature may need to contain
guidance on acceptable methods/calibration procedures for
calculating MVM’s to gain industry practice consistency
• Calibration of MVM models can be challenging and will
significantly affect the results
19
Financial Statement Presentation Issues (1)
• Any financial presentation separating current versus prior
accident year will require allocation judgments across all
accident years
• Level of disclosure for prior accident year development can
influence usefulness of information to financial statement
reader
20
Financial Statement Presentation Issues (2)
• Comprehensive, detailed disclosures could include impacts
from:
– Changes in undiscounted (mean) loss reserve best estimate
in comparison to the recorded loss reserve
– Natural unwinding of the discount amount
– Natural unwinding of the MVM as payments are made
– Changes in effective interest rate
– Changes in payout pattern
– Changes in the measure of MVM variation
– Changes in the MVM model calibration
21