liability driven investment chris nichols, standard life investments february 2006

Liability Driven InvestmentChris Nichols, Standard Life Investments

February 2006

2

Agenda

• Background / Liability Driven Investment Process

• Hedging Strategies

• Dynamic Market Risk Allocation

• Risk Versus Liabilities

• Risk Monitoring

Taking only risk that is rewarded and managed

3

Market Background

Liability Driven Investment Process

4

Traditional pension fund asset management

ALM Study

Scheme Actuary, Investment Consultants

• Liabilities

• Risk Appetite

• Long-term view of Asset returns

Strategic

asset

allocation

Manager Selection

Investment Consultants

Investment

mandates for

each asset class

Alpha management

Timescale: 10-20 years 1-3 years

Disconnect between scheme objectives and asset management

5

Results of Strategic Asset Allocation

• Deficits caused by falling interest rates and longevity increases

• Investment mandates were related to markets not liabilities

• So whilst investors beat the benchmark they failed against requirements

The status-quo for pension fund asset management is open to challenge

Source: Standard Life Investments

80

90

100

110

120

130

140

Jul-9

9

Jan-

00

Jul-0

0

Jan-

01

Jul-0

1

Jan-

02

Jul-0

2

Jan-

03

Jul-0

3

Jan-

04

Jul-0

4

Jan-

05

Benchmark Benchmark +1% Liability Growth

6

The focus of investment mandates

Tactical asset allocation

Strategic Benchmark

Scheme Liabilities

Benchmark risk

Stock Selection

Risk budget

Actual risk

Target return

1.0% 1.5%

1.0% 1.5%

1.0% 2.0%

0%

12.5%

Long-term: Real return from asset class allocation

Short-term: ????

Traditional mandates do not manage all short-term risk

7

Impact of investment timescales

• The excess return from equities over bonds has been 5% per annum over long periods

• We would not expect it to be exactly 5% over 3 year intervals

• But how often has it been within 2% of this level over 3 year periods?

• Answer: less than 25% of the time

• The long run excess return expectation will be wrong in 75% of three year periods

Distribution of excess returns from equities over bonds as a function of time horizon

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Time Horizon (Years)

Ret

urn

(%

)

Bounds

Outer Deciles

Median

Source: Standard Life Investments, Nov 2004

8

Impact of shorter timescales on return history

• As timescales reduce, risk and return expectations vary dramatically

10 years 3 years

Return pa

Risk pa

Return pa

Risk pa

UK equities 11.1% 10.3% 8.7% 16.4%

UK bonds 9.9% 4.2% 9.4% 6.2%

Overseas equities 9.5% 9.4% 6.8% 17.8%

Index-linked bonds 8.3% 2.8% 8.0% 4.9%

Property 9.2% 3.7% 9.1% 7.7%

Source: Datastream rolling returns, 31/12/86 – 31/12/04

• Shorter timescales have a significant impact on risk and return data• True for all asset classes, not just equity

9

Impact on traditional methodologies

Bond Equity Efficient FrontierSource Datastream: 31/12/1977-31/12/2004

9.0%

10.0%

11.0%

12.0%

13.0%

14.0%

15.0%

6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0%

Risk (pa)

Ret

urn

(p

a)

A traditional efficient frontier using all available data

100% bonds

100% equity

10


Bond Equity Efficient Frontier, Overlaid with Ranges for Bond Equity Risk Return Points: 10 year rolling windows

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0%

Risk (pa)

Ret

urn

(p

a)

Efficient Frontier

10 year rolling windows

The ‘area of possibility’ for a traditional efficient frontier, using 10 year rolling data windows

Source: Datastream 31/12/1977 - 31/12/2004

11


• The ‘area of possibility’ for a 3 year rolling data windows• The efficient frontier breaks down on these timescales

Bond Equity Efficient Frontier, Overlaid with Ranges for Bond Equity Risk Return Points: 3 and 10 year rolling windows

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0%

Risk (pa)

Ret

urn

(p

a)

Efficient Frontier



Source: Datastream 31/12/1977 - 31/12/2004

12

Conclusions for portfolio design

• Short-term measurements forces investors to be concerned with short-term portfolio returns, relative to the liabilities

• Long-term historic return data has little correlation with short-term return data

• Traditional methodologies of return optimisation and risk diversification using historical data fall apart on a short-term view

• A good solution will therefore ensure

• that all market risk is optimally managed in relation to the liabilities

• that risk monitoring and controls relate to the key risks

Informed investment view of short-term returns from different areas of market risk required

13

Liability driven process

Scheme Specific Funding Objective

Scheme Actuary, Investment Consultants

Liabilities

Risk Appetite

Funding Objective

Investment objective

Risk budget

Alpha strategy

Manager Selection

Investment Consultants

Investment

mandates

Beta management

Alpha management

Timescale: 3-5 years

Continuum established between liabilities and assets

14

LDI Solution Spectrum

MITIGATINGHEDGING

Rationale • Full matching impossible due to longevity risk

• Shares features of traditional methodologies – within comfort zone

• Natural first step towards a full liability driven approach

• Management processes altered from traditional methodologies

• Likely to involve prolonged buyer education process

Approach• Immunisation funds • Duration / Cashflow Matching

• Inflation Overlay

• Dynamic Market Risk Allocation (DMRA)

• RPI+ and libor+ strategies

MATCHING

Risk Elimination Risk Management

Comfort Zone

15

Pragmatism v’s Perfection

• Possible to devise an investment solution that aims to match out a set of projected liabilities

• As well as expensive, it is unnecessary and impractical

• Where non-investment risks are included it is not possible to produce an asset management strategy that takes away all risk

Tracking error versus uncertain cashflows A measure of total investment and non-investment risks in liability benchmark

Avoid an over-engineered solution

Source: Watson Wyatt Ltd (LIABILITY DRIVEN BENCHMARKS FOR UK DEFINED BENEFIT PENSION SCHEMES, 21 June 05)

16

“Modified Duration” = Interest rate sensitivity

Measuring the impact of interest rate movements

HighLow

£

High

Low

Interest Rates

Value of Scheme Assets

Value of Pension Liabilities

Scheme deficit increases if interest

rates fall

• Changes in interest rates are amplified in their effect on Assets and Liabilities

• Asset and Liability values often change by different amounts making funding volatile

• The rate at which the value changes is measurable and called “Modified Duration”

• It is very important to manage the overall modified duration risk versus the liabilities

• Much greater impact than performance versus a standard bond benchmark

• There are investment strategies to limit the difference between Asset and Liability movements

• These strategies reduce the volatility of a scheme’s funding rate

17

Duration Mismatch Example

• Assume scheme liabilities are valued using the AA bond yield

• Scheme assets invested in FTSE UK Gilts All Stocks

• Current AA bond yield is 5%

• Present Value (PV) of liabilities = £100m = Asset Value

• If yield falls by 1%:

• PV of liabilities rises to £115m

• Value of scheme assets rise to £107m

• Result = Deficit of £8m

18

Traditional bond fund options

• Government bond portfolios• Gilt fund duration 7.6• Long bond fund duration 13.4• IL bond fund duration 12.0• Overseas bond fund duration 5.9

• Corporate bond portfolios• Corporate bond fund duration 7.7• Long corporate bond fund duration 10.8

• Government and corporate bond portfolio• UK Fixed Interest fund duration 7.7

Benchmarks may bear little resemblance to scheme liabilities

19

To whom does this matter most?

• Companies where the scheme liabilities are large relative to shareholder funds / the size of the parent company

• Where the liabilities are longer dated than in this example

• Where a substantial proportion of scheme assets are invested in other asset classes that are insensitive to interest rates

• Investing 50% in equities for example would mean the scheme assets would only respond half as much to an interest rate change

• The impact could be nearer 15% of the fund

20

Example Liability vs Benchmark Cashflows

Source: CreditDelta, UBS

Benchmark Liabilities

Liabilities

Benchmark

21

Risk Analysis


• Risk is decomposed into interest rate and spread risk• Desire to remove interest rate curve risk

Benchmark

Benchmark vs Liabilities

22

Sensitivity to Swap Curve Changes


• LDD1 at given tenor point indicates change in value for +1bp shift in rate

Indicates liabilities are longer duration

23

Calculating the Required Swap Hedge


• Assume purchase of swaps with 5, 10 and 30yr maturity• Calculate nominals required to hedge LDD1 mismatch

• Cost of each swap is (spread from Mid) * magnitude of LDD1• At 1bp spread, cost of swaps is £209,770• Cost represents 6.5bps of total fund

24

Sensitivity to Swap Curve Changes


• LDD1s are matched at selected tenor points

Only small mismatches remain

25

Risk Analysis


• Interest rate risk becomes small with swaps• Tracking error drops from 2.8% to 1.5%

Benchmark

vs Liabilities

26

Duration Products

• Custom swap overlay is available to seg funds• Collateral management • Legal requirements

• Pooled Fund Alternatives:• Bucketed Funds• Actuarially priced pooled funds

• Share the objective of giving duration

• Differences:• Legal structure • Credit spread• Active management

27

Liability Replicating Portfolio

BENCHMARK % of category % of total INDEX-LINKED GILTS

50.00%

2.5% index-linked Treasury 2016 22.50% 11.25% 2.5% index-linked Treasury 2024 22.50% 11.25% 4.125% index-linked Treasury 2030 20.00% 10.00% 2% index-linked Treasury 2035 17.50% 8.75% 1.25% index-linked Treasury 2055 17.50% 8.75% ZERO COUPON LPI SWAPS

30.00%

5 year 10.00% 3.00% 10 year 10.00% 3.00% 15 year 20.00% 6.00% 20 year 20.00% 6.00% 25 year 20.00% 6.00% 30 year 20.00% 6.00% ZERO COUPON FIXED INTEREST SWAPS

20.00%

5 year 15.00% 3.00% 10 year 15.00% 3.00% 15 year 15.00% 3.00% 20 year 20.00% 4.00% 25 year 20.00% 4.00% 30 year 15.00% 3.00%

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065

Fixed Increases (0%) RPI Increases LPI subject to 5% LPI subject to 2.5%

Manage market risk against the liabilities

28

Mapping the solution to the mandate

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

07 09 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79

Pensions: increasing at lpi

Pensions: increasing at 0%

Lump sums

Liability Replicating Portfolio

ILG Gilts

ZC LPI Swaps

ZC IR Swaps

Investment Assets

Swap Asset Benchmark to LIBOR

50% ILG (>5 year index)Alpha Target + 60 bps

50% ML, £, Non-gilt, Ex AAAAlpha Target +80 bpsBeta Target +40 bps

LIBOR + 90bps gross

Swap LIBOR to Liability Replicating Portfolio

Inflation swaps

LPI Swaps

Plain Vanilla IR Swaps

+

+

Benchmark + 75bps Benchmark + 75bps + fees

Netting off of

positions

Scheme Liabilities

29

LDI Solution Spectrum

MITIGATINGHEDGING

Rationale • Full matching impossible due to longevity risk

• Shares features of traditional methodologies – within comfort zone

• Natural first step towards a full liability driven approach

• Management processes altered from traditional methodologies

• Likely to involve prolonged buyer education process

Approach• Immunisation funds • Duration / Cashflow Matching

• Inflation Overlay

• Dynamic Market Risk Allocation (DMRA)

• RPI+ and libor+ strategies

MATCHING

Risk Elimination Risk Management

Comfort Zone

30

Dynamic Market Risk Allocation (DMRA)

• Increasing focus on avoiding short-term asset/liability mismatch

• Market risk is the principle contributor of risk relative to the liabilities

• Logical movement from static to dynamic market risk positions

• Standard Life Investments’ solution:• Dynamic management of market risk based on three year view

• Active views on all areas of market risk,

• Optimal portfolios created to meet individual client requirements

Risk budget optimally deployed at all times

31

Exploiting an uncrowded area

The ‘new balanced’ approach

• Fund managers and traders look to add value over short time scales

• Numerous active participants limit opportunities to add value over short term time horizons

• DMRA is about exploiting medium term opportunities

• 3 to 5 year time horizon

• Look to take as many diverse views as possible to exploit benchmark risk

• Strategies to provide downside protection versus liabilities


Economics

Demographics

Value

Funds Flow

Earnings upgrades

Recovery stocks

Pairs trading

Convert arbitrageSource of

added value

10y-50y

WP funds etc

-Active participants

1y-10y

DMRA

1m – 1y

Fund Managers

1d -1m

Traders

Economics

Demographics

Value

Funds Flow

Earnings upgrades

Recovery stocks

Pairs trading

Convert arbitrageSource of

added value

10y-50y

WP funds etc

-Active participants

1y-10y

DMRA

1m – 1y

Fund Managers

1d -1m

Traders

Investment time horizon

• Is it plausible?

• Has it made money?

• Is it theoretically proven?

32


Evidence for Opportunities at Longer Timescales

Non-Outlier MaxNon-Outlier Min

75%25%

Median

Box Plot: 1-Year Equivalent Volatility of UK Equity Market

Total Real Return Data: 1900 - 2002, Source BZW Equity Gilt Study

Comparison with 40 random shuffled BZW data surrogates

Non-overlapping return runs of length n

1 Y

ear

Eq

uiv

ale

nt

Vola

tility

y_1

0.06

0.10

0.14

0.18

0.22

0.26

0.30

0.34

bzw sur

y_2

bzw sur

y_4

bzw sur

y_8

bzw sur

y_16

bzw sur

33


Evidence in individual stock returns

Non-Outlier MaxNon-Outlier Min

75%25%

Median

Box Plot: 1-Month Equivalent Volatility of Shell Stock

Total Return Data 31/12/69 - 30/11/04, Source DataStream

Comparison with 40 random shuffled Shell data surrogates

Non-overlapping return runs of length n

1 M

onth

Equiv

ale

nt V

ola

tility

m_1

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.11

SHEL(RI) sur

m_2

SHEL(RI) sur

m_4

SHEL(RI) sur

m_8

SHEL(RI) sur

m_16

SHEL(RI) sur

m_32

SHEL(RI) sur

34

Areas of market risk

• Market risk positions can be very broadly based• Not just an equity bond call• The risks that should be brought to bear include

• FX risk

• Duration risk

• Credit risk

• Equity market risk – including regional and sector views

• Property market risk

• Commodities

• Volatility

• Optionality

Play the right team at the right time

35

Risk Return Options

Corporate Bonds

70:30

LMC

DMRA

50:50

Current

Equity

0

1

2

3

4

5

6

7

8

0 2 4 6 8 10 12 14 16 18 20

Risk relative to Liabilities

Re

turn

re

lati

ve

to

Lia

bil

iite

s

Example of Efficient Risk Deployment

36

Measuring Risk v’s Liabilities

• Ex-ante analysis:• Must allow for liabilities• Overcome shortcomings of historic data

• Ex-post analysis:• tracking error in absolute and relative terms:

• volatility of returns of asset pool

• volatility of relative returns

• V-masks

Risk monitoring and control must relate to the key risks versus liabilities

37

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

Month

ly R

etu

rnMeasures of variation and association

• General characteristics of individual time series can often be easily observed from graphs.

• Harder to determine the relationship, if any, between relative variations of two or more series.

• Related measures of Correlation and Covariance are used to quantify this behaviour.

0%

5%

10%

15%

20%

25%

30%

Monthly Return

Fre

quen

cy

Source Datastream: 31/01/1995 – 31/12/2004

38

World Equites ex UK vs S&P Comp

-20%

-15%

-10%

-5%

0%

5%

10%

15%

-20% -15% -10% -5% 0% 5% 10% 15%

Interpreting correlation

• A scatter plot helps illustrate correlation

• The example shows monthly returns on two indices plotted against one another. The proximity to a ‘regression line’ through the data shows there is strong, positive, correlation between the two indices

• A positively sloping line indicates positive correlation – returns on the assets move together

• A negatively sloping line means negative correlation – asset returns move in opposite directions

• The extreme cases of CorXY =1 and CorXY = -1 (perfect correlation) occur only if all points lie on a straight line

• If CorXY = 0, the assets returns are uncorrelated

CorXY=0.96

Source Datastream: 31/01/1995 – 31/12/2004

39

Covariance Matrix

• When there are many variables, the co-variation between all possible pairs can be conveniently represented in a Covariance Matrix. E.g for 3 variables:

Mean: 7.82% 9.20% -4.37% 5.88% 8.02% 9.49% 7.95% 10.73% 5.36%Standard Dev: 13.84% 17.88% 21.73% 17.04% 5.38% 7.59% 4.32% 1.38% 0.35%Correlation coeffs:

FT All Share S&P Topix World ex UK IL Gilts Long Gilts UK Corp Property CashFT All Share 1.00S&P 0.82 1.00Topix 0.47 0.48 1.00World ex UK 0.87 0.96 0.64 1.00IL Gilts 0.11 0.09 0.11 0.12 1.00Long Gilts -0.04 0.02 -0.02 0.00 0.69 1.00UK Corp 0.01 -0.01 -0.05 -0.01 0.64 0.88 1.00Property 0.02 -0.06 0.05 -0.01 0.08 0.04 -0.04 1.00Cash 0.13 0.18 -0.10 0.10 0.21 0.27 0.22 -0.10 1.00

• Same format used for correlations. Elements along the leading diagonal are unity.

• Example:

40

• Tracking error is the standard deviation of the difference in returns between a portfolio and a benchmark. It is calculated as

Calculating Ex-Ante Tracking Error

where Cov is the covariance matrix and B is the vector of bets away from a neutral benchmark position (i.e. the difference in percentage weight, for each asset class, between the portfolio and the benchmark). The sum of bets across all asset classes is 0.

• Extension to TE versus liabilities is achieved by treating liabilities, represented by a replicating portfolio, as a separate asset class. Covariance is calculated in the usual way. Weights are then against a neutral position, with the liability weight taken as –100%.

TE = 5.01%

41

Overcoming the historic data problem

Inputs:• Core & custom data pack• Asset class desk experts• Quant input

For each view the SIG produces:• Return expectations• Upside and downside expectations• Conviction• Correlation

Directly driving portfolio construction & risk monitoring

Asset ClassCentral Return

Estimate

Upside Return

Estimate

Downside Return

Estimate

Standard Deviation Estimate

UK Equity 10.0% 18.0% -3.0% 8.2%Global Equity 12.0% 20.0% -5.0% 9.8%Property 6.0% 8.0% -6.0% 5.5%Credit 4.6% 5.0% 3.6% 0.5%Japanese Government Bonds (hedged) 6.5% 10.0% 3.0% 2.7%Global Index Linked Bonds (hedged) 4.0% 6.0% 1.0% 2.0%Cash 4.0% 4.0% 4.0% 0.0%

Strategic Investment Group

Dr Julian CouttsHead of Quantitative Risk

(Advisory role)

Sarah SmartInvestment Director

(Secretary)

Keith SkeochChief Executive

Standard Life Investments

Lance PhillipsHead of Overseas Equities

Neil MathesonVP and Economist

Standard Life Canada

Andrew SutherlandInvestment Director

Fixed Interest

Euan MunroHead of Strategic Solutions

Chairman

Historic Risk

Historic Correlation

+ Conviction

Expected RiskClient

Portfolio V - Masks


42

Ex-Post Risk / Monitoring

Use V-masks for return generating processes:

“Is the current experience plausible, within the context of our original opinion of the risk and return inherent in this particular position”

0.80

0.85

0.90

0.95

1.00

1.05

1.10

Nov-00 May-01 Nov-01 May-02 Nov-02 May-03 Nov-03

Cum

ulat

ive

Val

ue A

dded

Monthly Cumulative ReturnUpper and Lower Boundary

Confidence we will not overrun the budget

43

Maths of the generalised V Mask

• Excess value is proposed to be R(T) = N(μT, σ2T)• “Funnel of Doubt”

• Expected value R(T) = exp(μT)• UB(T) = exp{μT + 1.65*σT1/2} etc

• Now turn “funnel of doubt” backwards…• To end up at Actual(T) on the above return process, the expected value should have

come from• R(t) = Actual(T)*exp{- μ(T-t)}• UB(t) = Actual(T)*exp{- [μ(T-t) + 1.65 *σ(T-t)1/2]}

• Interpret this as…

“To have ended up here, with the proposed return process, we should have come from inside the (backwards) “funnel of doubt”. If the trajectory actually falls outside the UB, then the process actually operating was NOT that proposed, to UB level of certainty (1.65 = 95% certainty.)”

THE LINE’S OUTSIDE, THE STORY IS WRONG, SO REVIEW IT…

44


DMRA V-Mask Monitor: Stop Losses in PracticeUK Credit

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

1.1000

Date

Cum

ulat

ive

Valu

e A

dded

Daily Cumulative Return

Lower Boundary

Upper Boundary

UK Equity

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

1.1000

Date

Cum

ulat

ive

Valu

e A

dded


Lower Boundary

Upper Boundary

Global Equity

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

1.1000

DateC

umul

ativ

e Va

lue

Add

ed


Lower Boundary

Upper Boundary

Global Index Linked Bonds Hedged

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

1.1000

Date

Cum

ulat

ive

Valu

e A

dded


Lower Boundary

Upper Boundary

JGB Hedged

0.8000

0.8500

0.9000

0.9500

1.0000

1.0500

1.1000

Date

Cum

ulat

ive

Valu

e A

dded


Lower Boundary

Upper Boundary

45

Benefits of taking a dynamic approach

• Broadens the investment universe• Examines the return potential of all areas of market risk

• Targets asymmetric return expectations• Positioning in the range informs those to harness and those to avoid

• Flexing the position to respond to specific circumstances• Taking a contrarian view on an asymmetric position can protect in downside

scenarios – implied volatility for example

• Responsive to the Investor’s risk appetite• Adjusting the hedging strategy depending on the Sponsor’s ability to make

additional contributions

Investment expertise guided by quantitative discipline

46

Example: Long volatility

• Strategy:• hold out of the money calls to

access desired additional equity exposure and exposure to implied volatility

• Rationale:• Asymmetric return expectation:

• implied volatility currently right at the bottom of its range

• expect it can go a lot higher but not much lower

• rise in dynamic hedging means there are many institutions that will be forced traders if there is a big move in any direction

Source: Bloomberg

LDI is about establishing a

transparent link between liabilities and assets and minimising uncompensated risks.

Hugh Cutler, Pensions

Management, 01/04/05

Liability Driven

Investing is a risk

preference based

approach which can be

used to complement

or totally replace current

strategies. Finance IQ Conference,

04/06

.. portable alpha strategies, which is another way of referring to LDI. Global

Investor Magazine, 08/05

..liability-driven investing, which seeks to match more closely the returns generated by a pension

fund’s assets with its commitments. Financial News, 02/01/06

“LDI … the process whereby an investment strategy is set with explicit reference to a specific set of liabilities.” Mercer Investment

Consulting

Liability-driven investing focuses on managing a plan’s liability risk while providing multiple sources of excess

return. Jane Tisdale, SsgA, 17/10/05

LDI relates to the practice of using investment tools such as derivatives to help funds meet their payouts to investors even though markets may

be volatile. The Standard (Hong Kong), 21/12/04

‘liability-driven investing’ (matching liability growth to the extent possible), Watson

Wyatt, Canada

'liability-driven investment strategies', which involves

swapping the income which they will receive from their

long-dated bonds with instruments which better match their liabilities. The

Observer, 22/01/06

48

What is LDI?

• A range of strategies and novel processes

• That are evolving in response to the problems pension schemes are facing• Mark – to market

• Visibility in accounts

• Making optimum use of available risk budgets• Specifically, avoiding unmanaged and unrewarded risk

• Employing those closest to the market to

• Perform against liabilities

• Over timescales that are now appropriate

liability driven investment chris nichols, standard life investments february 2006

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