A Rating Agency Perspective OnLife Insurer Risk Management

Presented by:

Joel LevineVice President & Senior AnalystLife Insurance Group

Presented by:

Joel LevineVice President & Senior AnalystLife Insurance Group

April 26, 2004

2

Presentation Outline Usefulness of available risk management

information - rating agency focus versus management information and regulatory reporting

What rating agencies worry about

– Lessons from past failures

– Growing complexity of business – GMDB and UL no

lapse guarantees

– Secondary market for insurance contracts

Hedging VA guaranteed living benefits

Conclusions

3

A Lot Of Data,

Not Enough

Information

4

Rating Agency Focus Credit analysis focuses on operating entity and

enterprise level (holding co.) financial condition

Analyzes the organization’s ability to withstand stress from multiple sources of risk

– Comprehensive in scope: liquidity, asset default,

concentration, interest rate, market, inflation,

actuarial, competitive, reputation, regulatory,

litigation, merger/business integration, etc.

– Ongoing concern point of view, except for liquidity

– Compound interaction among risk factors, not just

one at a time

– Medium term horizon

5

Issuer Management Reporting Focus

Many issuers are managed as decentralized business segments with fragmented reporting

– Separately managed investment portfolios with

tailored ALM requirements

– Management reports (e.g., scenario projections)

are produced on an independent, business segment

basis

– Surplus assets are generally ignored

– Corporate (holding co) balance sheet and income

components are ignored

– Affiliated entities are excluded

– Scenario analysis on a run-off basis

6

Regulatory Reporting Focus Cash flow testing provides useful information,

but has its limitations

– Reserve adequacy focus as opposed to company

performance (e.g., capitalization level over time)

– Not all businesses/products are included

– Performed on a run-off basis

– Surplus assets are ignored

– Corporate (holding co) balance sheet and income

components are ignored

– Affiliated entities are excluded

– Primarily focused on interest rate risk (except for

NAIC C-3 Phase II)

– SAP only, no GAAP information

7

Desirable Supplementary Risk Mgt Reporting

Goal is to achieve a clearer understanding of the drivers of performance and the potential for significant losses – tail risk

– Break-even scenario analysis - what it would take

to impair the organization over a given time

horizon

– Stress economic scenario analysis

– VaR/EaR analysis – a more comprehensive analysis

of risk that would reflect compound interaction of

various risk factors

– Fair value accounting

– Embedded value – sensitivity analysis

8

What Do Rating

Agency Analysts

Worry About?

9

Lessons From Past Failures

General American (1999) – liquidity risk of 7-day putable funding agreements, reinsurance with a weak company, loss of confidence by institutional investors

Confederation Life (1994) – heavy investment portfolio losses in real estate and commercial mortgages, unsuccessful acquisition strategy

First Capital Holding (1991) and First Executive (1991) – losses on high yield bonds, increase in policy surrenders impairing company liquidity

10

The Business Is Getting More Complex and Risky Increasingly, new products are being created with a

focus on guarantee elements

– Equity market uncertainty has made investors more

receptive to floors on returns

– Guarantees have become huge drivers of annuity sales

(GMWB, GMIB)

– Guarantees have crept into life insurance products -

e.g., UL no lapse guarantees (AXXX)

New embedded risks are extremely complex and require sophisticated modeling in order to understand and hedge them

Some companies are developing me-too designs without having a full appreciation of the risk

11

What Keeps A Rating Agency Analyst Up At Night?

Issuers that engage in products/activities that require undemonstrated competencies and/or that lack a cohesive process to manage them

– Does the issuer recognize the risks it has assumed

– e.g., dollar for dollar partial withdrawals?

– VA secondary guarantees – when did life insurers

become expert managers/traders of market risk?

– Does the issuer currently have or can it acquire

sufficient resources to manage the risks?

– Is senior management committed?

– Who’s accountable for the process –

nobody/everybody, multiple committees, CRO?

12

Lesson Learned – GMDB Is More Than Actuarial Risk

Historically, life insurers accumulated actuarial risks and managed them using risk selection, diversification, and reinsurance

GMDB reinsurers tried to “diversify” by writing new business at different points in time (over a market cycle) – benefit payment on death only would provide “diversification” by exercise date

Back-testing using historical equity market prices validated the “insignificant” economic risk of GMDB; risk neutral valuation not well understood

Ignored the systematic nature of market risk and the “temporary” impacts on statutory surplus

13

A Case Study – UL No Lapse Guarantee UL no-lapse guarantees expose issuers to

potentially significant and systematic risks

– Simultaneous occurrence of low lapse rates and low

interest rates may produce very large losses;

moderate adverse deviations can create material

losses

– Moody’s is concerned that issuers’ pricing lapse

assumptions may be too high and rely upon

naive/irrational policyholder behavior

– Pattern of statutory earnings under adverse lapse

scenario is for losses to emerge in later policy

durations; masking of the problem in the early

years

14

A Case Study – UL No Lapse Guarantee (Cont’d)

Portfolio Yield Lapse Rate 7% 5% 3%

6% 15,611 -20,327 -39,435 4% -15,232 -48,246 -64,886 2% -64,997 -91,755 -103,530

Present Value Of Profits At 9% Discount Rate For A $1 Million Policy

15

A Case Study – UL No Lapse Guarantee (Cont’d)

-$5,000

-$3,000

-$1,000

$1,000

$3,000

$5,000

$7,000

$9,000

$11,000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37

Normal

Lapse 4%

Lapse 2%

Statutory Profits With Varying Lapse Rates

16

How Will Middle-Tier Issuers Cope? VA secondary guarantee hedging requires a

significant and expensive commitment of resources

– Systems development to integrate policy admin system

with new actuarial modeling systems

– Hardware – massive amount of policy records and many

thousands of stochastic simulations

– Accounting support and internal controls

– Experienced traders

Outsource it? – can I afford it? - actuarial consulting firm, derivatives dealer with a turnkey program, emerging reinsurance programs

Can a middle-tier issuer remain competitive?

17

Secondary Market For Insurance Contracts

Firms are being formed to facilitate a secondary market for annuity contracts and life insurance policies – and arbitrage the “irrational” policyholder behavior pricing assumptions

– IBuyAnnuities.com

– CoventryFirst.com

Wall Street capital has not been deployed in a significant way to-date, but that could change

Potentially dire implications for some issuers

– Lapse assumptions for in-the-money GMDB,

GMWB/GMIB, $ for $ partial withdrawals

– Mortality assumptions for life insurance policies

18

Analyze This –

What’s The “Right”

Objective For A

Hedging Strategy?

19

VA Secondary Guarantee Hedging Objectives Reduce the tail risk – potential for large

economic (pv of net cash flows) losses

But, subject to external constraints: NAIC RBC, rating agency capital requirements, preserve company shareholder dividend capacity, maximum tolerable GAAP income loss, etc.

Competing constraints make it difficult to resolve analytically (e.g., with an optimizer)

For a rating agency, reported GAAP net income is not necessarily controlling; economics are the primary concern

20

Secondary Guarantees Subject To FAS 133

Both assets and liabilities are marked-to-market, so GAAP results are reasonably predictable with one major exception

Popular dynamic hedging practice is to match the liability “greeks” – delta, rho, gamma, vega, cross-sensitivities; trade-off between effectiveness and cost

Arguments made that implied volatility for long-term derivatives fluctuates and tends to be mean-reverting: makes it difficult to match the liabilities’ sensitivity to changes in volatility (vega)

21

Secondary Guarantees Subject To FAS 133 (Cont’d)

Historical Realized Volatility On S&P 500 Options

Notes1. Underlying S&P 500 data from Bloomberg (daily frequency from 1/2/1941 to 3/26/2004)2. Historical volatility numbers are based off daily S&P 500 returns adjusted by an annualization factor 1

5% and 95% Confidence IntervalMedian

0%

5%

10%

15%

20%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

10-Year Rolling Historical VolatilityPer Annum (%)

0%

5%

10%

15%

20%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

10-Year Rolling Historical VolatilityPer Annum (%)

0%

20%

40%

60%

80%

100%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

22 Day Rolling Historical VolatilityPer Annum (%)

0%

20%

40%

60%

80%

100%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

22 Day Rolling Historical VolatilityPer Annum (%)

Mean:12.72%, Median: 11.24%, 5% Conf: 5.82%, 95% Conf: 24.21%, Current Vol: 14.52%

0%5%

10%15%20%

25%30%

35%40%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

1-Year Rolling Historical VolatilityPer Annum (%)

0%5%

10%15%20%

25%30%

35%40%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

1-Year Rolling Historical VolatilityPer Annum (%)

0200

400600

8001000

12001400

1600

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

S&P 500 Price IndexLevel (Points)

0200

400600

8001000

12001400

1600

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

S&P 500 Price IndexLevel (Points)

0%

5%

10%

15%

20%

25%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

3-Year Rolling Historical VolatilityPer Annum (%)

0%

5%

10%

15%

20%

25%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

3-Year Rolling Historical VolatilityPer Annum (%)

0%

5%

10%

15%

20%

25%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

5-Year Rolling Historical VolatilityPer Annum (%)

0%

5%

10%

15%

20%

25%

Feb

-41

May

-46

Aug

-51

Nov

-56

Feb

-62

May

-67

Aug

-72

Nov

-77

Feb

-83

May

-88

Aug

-93

Dec

-98

Mar

-04

5-Year Rolling Historical VolatilityPer Annum (%)

Mean:13.68%, Median: 12.90%, 5% Conf: 9.66%,95% Conf: 20.29%, Current Vol: 20.91%

Mean:13.51%, Median: 13.56%, 5% Conf: 9.90%,95% Conf: 17.47%, Current Vol: 18.18%

Mean:13.47%, Median: 12.52%, 5% Conf: 7.96%,95% Conf: 22.40%, Current Vol: 13.75%

Mean:13.65%, Median: 12.53%, 5% Conf: 8.92%,95% Conf: 22.80%, Current Vol: 21.06%

22

Secondary Guarantees Subject To FAS 133 (Cont’d)

If long-term implied volatility is “noise”, can it be safely ignored – and could one hedge with futures only?

– Maybe, but one shouldn’t ignore the risk that delta

may change significantly with large market moves

(gamma) – therefore, may need some option

exposure

Under NAIC C3 Phase II, stochastic simulations will be used to determine SAP reserve and RBC levels

23

Secondary Guarantees Subject To FAS 133 (Cont’d)

NAIC C3 Phase II methodology ignores spot market parameters such as implied volatility

– Hedging vega will not necessarily produce a more

stable SAP financial result

– Responses of the hedge assets to changes in

implied volatility will not be offset by

corresponding changes in the values of the SAP

liabilities

24

Emerging Direction Of Hedging VA Secondary Guarantees Index hedge – long-term options covering well

defined risks, while the insurer retains the uncertain, unhedgeable risks

– Hedge payoff based upon an assumed basket of indices,

with provision for basket changes over time, and pricing

that varies accordingly

– Insurer retains the basis risk – basket vs actual fund

performance

– Long-tenor hedge whose payoff approximates the

payoff pattern of the particular GLB design; issuer

would assume the basis risk between the actual VA GLB

payoff and the approximation

– Contractholder exercise efficiency would be another

source of error

25

Future Direction Of Hedging VA Secondary Guarantees Index hedge (cont’d)

– Rating implications would depend upon the

effectiveness of the hedge

• Insurer would need to demonstrate effectiveness

through stochastic modeling that the basis risk

would be manageable – i.e., the tail risk (economic

perspective) would not be excessive

– SAP impact would need to be considered – how

would statutory surplus be impacted using such a

hedge under various scenarios?

• SAP surplus impact might be mitigated by using a

combination reinsurance/derivatives structure

26

Conclusions

27

Conclusions Life insurers are rapidly expanding into new types of less

familiar risks – e.g., trading market volatility

Management and regulatory reporting needs to catch up

with the complexity of these new risks – rapid strides are

being made

Risk management processes have been developed and are

evolving - but are untested under extreme market conditions

Basis risk and modeling risk inherent in hedging are difficult

to quantify

Insurance business has become more risky - assessment of

risk management skills has become more challenging and

critical to the credit ratings process