CAS Fair Value Project

Casualty Actuaries in Europe Spring Meeting23 April 2004

E. Daniel Thomas(1) 646-471-5746

edward.d.thomas@us.pwc.com

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Disclaimer

• The opinions expressed should not be regarded as representing the views of the CAS or of PricewaterhouseCoopers LLP

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Presentation Outline

• CAS Project Objectives

• Data Characteristics

• Modeling Approaches

• Preliminary Findings

• Significant Issues

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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

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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

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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)

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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

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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

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Impact of Discount Factor Models

Duration vs. Matched to Yield-CurveSum of Total Discount YearLine Co code Interest Method 1998 1999 2000 2001 2002Personal Auto Liability D (L) Duration 8.0% 10.4% 8.5% 4.5% 2.5%

Matched 8.0% 10.6% 8.4% 5.9% 3.5%E (L) Duration 5.8% 7.7% 6.9% 4.4% 2.3%

Matched 5.8% 7.9% 6.7% 5.6% 3.2%C (M) Duration 8.2% 10.0% 8.2% 4.3% 2.3%

Matched 8.2% 10.2% 8.0% 5.6% 3.2%F (M) Duration 7.2% 9.3% 7.9% 4.4% 2.5%

Matched 7.2% 9.4% 7.8% 5.6% 3.4%G (M) Duration 6.7% 9.3% 7.8% 3.9% 2.2%

Matched 6.7% 9.5% 7.7% 5.0% 3.0%A (S) Duration 7.5% 7.5% 6.9% 4.5% 0.7%

Matched 7.5% 7.7% 6.7% 5.9% 0.7%B (S) Duration 8.2% 10.0% 8.2% 4.6% 2.7%

Matched 8.2% 10.2% 8.1% 5.9% 3.8%H (S) Duration 7.3% 9.6% 7.8% 4.9% 2.8%

Matched 7.3% 9.7% 7.6% 6.1% 3.8%I (S) Duration 6.8% 7.7% 7.9% 3.7% 2.4%

Matched 6.8% 7.9% 7.8% 4.9% 3.3%V (S) Duration 4.4% 5.6% 5.5% 2.5% 1.6%

Matched 4.4% 5.8% 5.3% 3.3% 2.1%

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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)

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MVM by Company –Personal Auto Liability

MVM by Company at Year-End 2002

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2 4 6 8 10 12

Company

MV

M %

ROC MVM

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MVM by Company for One Method – Personal Auto Liability

MVM by Company at Each Year-End (Development Model)

0%

20%

40%

60%

80%

100%

120%

1997 1998 1999 2000 2001 2002 2003

Year-End

MV

M (

%)

A (S)

B (S)

H (S)

I (S)

V (S)

C (M)

F (M)

G (M)

D (L)

E (L)

Average

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MVM by Method for One Company – Personal Auto Liability

MVM by Method - Company C(M)

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

1998 1999 2000 2001 2002Year-End

MV

M %

Develop SD

Stoch SD

Percentile

ROC

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MVM by Company Size – Personal Auto Liability

MVM for All Companies and All years

0%

10%

20%

30%

40%

50%

60%

70%

0 200,000 400,000 600,000 800,000 1,000,000

Reserves ($000)

MV

M (

%)

MVM vs.CompanySize

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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

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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

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Significant Issues

• Modeling Issues

• MVM Estimation Issues

• Financial Statement Presentation Issues

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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)

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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

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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

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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