liability driven investment chris nichols, standard life investments february 2006
DESCRIPTION
Liability Driven Investment Chris Nichols, Standard Life Investments February 2006. 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. - PowerPoint PPT PresentationTRANSCRIPT
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
Impact on traditional methodologies
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
Impact on traditional methodologies
• 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
10 year rolling windows
3 year rolling windows
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
Source: CreditDelta, UBS
• 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
Source: CreditDelta, UBS
• 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
Source: CreditDelta, UBS
• 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
Source: CreditDelta, UBS
• LDD1s are matched at selected tenor points
Only small mismatches remain
25
Risk Analysis
Source: CreditDelta, UBS
• 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
Source: Standard Life Investments
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
Source: Standard Life Investments
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
Source: Standard Life Investments
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
Source: Standard Life Investments
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
Source: Standard Life Investments
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
Daily Cumulative Return
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
Daily Cumulative Return
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
Daily Cumulative Return
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
Daily Cumulative Return
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