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Liability Driven Investment

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Presentation on theme: "Liability Driven Investment"— Presentation transcript:

1 Liability Driven Investment
Chris Nichols, Standard Life Investments February 2006

2 Agenda Taking only risk that is rewarded and managed
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 Manager Selection Scheme Actuary, Investment Consultants Investment Consultants Liabilities Risk Appetite Long-term view of Asset returns Strategic asset allocation Investment mandates for each asset class Alpha management Traditional process designed to work on a long-term basis: short-term variations in asset values are not critical This works – AS LONG AS the sponsoring company exists in its current state in perpetuity to support the scheme This DOES NOT WORK if a significant event happens that crystallises the position when asset values < liabilities 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 80 90 100 110 120 130 140 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Benchmark Benchmark +1% Liability Growth There were two main problems The market risk relative to the liabilities was not being managed Almost all risk monitoring and control related to stock selection risk Hence a good solution will ensure that all market risk is managed in relation to the liabilities that risk monitoring and controls relate to the key risks Source: Standard Life Investments The status-quo for pension fund asset management is open to challenge

6 The focus of investment mandates
Scheme Liabilities Benchmark risk Strategic Benchmark Tactical asset allocation Stock Selection Risk budget Actual risk Target return 0% 12.5% Long-term: Real return from asset class allocation Short-term: ???? 1.0% 1.5% 1.0% 2.0% 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) Return (%) 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 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% 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
100% equity 100% bonds A traditional efficient frontier using all available data

10 Impact on traditional methodologies
Source: Datastream 31/12/ /12/2004 The ‘area of possibility’ for a traditional efficient frontier, using 10 year rolling data windows

11 Impact on traditional methodologies
Source: Datastream 31/12/ /12/2004 The ‘area of possibility’ for a 3 year rolling data windows The efficient frontier breaks down on these timescales

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 Manager Selection Scheme Actuary, Investment Consultants Investment Consultants Beta management Liabilities Risk Appetite Funding Objective Investment objective Risk budget Alpha strategy Investment mandates Alpha management Timescale: 3-5 years Continuum established between liabilities and assets

14 LDI Solution Spectrum MATCHING HEDGING MITIGATING Rationale Approach
Risk Elimination Risk Management Comfort Zone MATCHING HEDGING MITIGATING 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

15 Pragmatism v’s Perfection
Tracking error versus uncertain cashflows A measure of total investment and non-investment risks in liability benchmark 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 Source: Watson Wyatt Ltd (LIABILITY DRIVEN BENCHMARKS FOR UK DEFINED BENEFIT PENSION SCHEMES, 21 June 05) Avoid an over-engineered solution

16 “Modified Duration” = Interest rate sensitivity
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 High Scheme deficit increases if interest rates fall Value of Scheme Assets Value of Pension Liabilities Low Low High Interest Rates Measuring the impact of interest rate movements

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
Liabilities Benchmark Benchmark Liabilities Source: CreditDelta, UBS

21 Risk Analysis Risk is decomposed into interest rate and spread risk
Benchmark Benchmark vs Liabilities Risk is decomposed into interest rate and spread risk Desire to remove interest rate curve risk Source: CreditDelta, UBS

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 Source: CreditDelta, UBS

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 Source: CreditDelta, UBS

24 Sensitivity to Swap Curve Changes
LDD1s are matched at selected tenor points Only small mismatches remain Source: CreditDelta, UBS

25 Risk Analysis Interest rate risk becomes small with swaps
Benchmark vs Liabilities Interest rate risk becomes small with swaps Tracking error drops from 2.8% to 1.5% Source: CreditDelta, UBS

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
Manage market risk against the liabilities

28 Mapping the solution to the mandate
Benchmark + 75bps Benchmark + 75bps + fees Liability Replicating Portfolio ILG Gilts ZC LPI Swaps ZC IR Swaps Swap LIBOR to Liability Replicating Portfolio Inflation swaps LPI Swaps Plain Vanilla IR Swaps + Netting off of positions LIBOR + 90bps gross Scheme Liabilities Swap Asset Benchmark to LIBOR + Investment Assets 50% ILG (>5 year index) Alpha Target + 60 bps 50% ML, £, Non-gilt, Ex AAA Alpha Target +80 bps Beta Target +40 bps

29 LDI Solution Spectrum MATCHING HEDGING MITIGATING Rationale Approach
Risk Elimination Risk Management Comfort Zone MATCHING HEDGING MITIGATING 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

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 The ‘new balanced’ approach
Is it plausible? Has it made money? Is it theoretically proven? 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 Exploiting an uncrowded area

32 Evidence for Opportunities at Longer Timescales
Compare the volatilities of returns with different lengths of non-overlapping chain linked return data Normalise to SQRT(time window). All data should be identical if markets are IID Clearly this is not the case, but how significant is this? Previous work has found difficulty with lack of data: we have a new technique Compare to 40 random scrambled data series. This (on average) destroys any time series coherence, and allows us to assess significance. Explore at index level over long timescales (BZW Equity Gilt Study.) Explore at single stock level over long timescales (Shell) Source: Standard Life Investments

33 Evidence in individual stock returns
Source: Standard Life Investments

34 Areas of market risk Play the right team at the right time
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 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 Measures of variation and association
Source Datastream: 31/01/1995 – 31/12/2004 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.

38 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: Same format used for correlations. Elements along the leading diagonal are unity. Example:

40 Calculating Ex-Ante Tracking Error
Tracking error is the standard deviation of the difference in returns between a portfolio and a benchmark. It is calculated as 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
Strategic Investment Group Dr Julian Coutts Head of Quantitative Risk (Advisory role) Sarah Smart Investment Director (Secretary) Keith Skeoch Chief Executive Standard Life Investments Lance Phillips Head of Overseas Equities Neil Matheson VP and Economist Standard Life Canada Andrew Sutherland Fixed Interest Euan Munro Head of Strategic Solutions Chairman 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 + Conviction Source: Standard Life Investments Client Portfolio Historic Risk Expected Risk V - Masks Historic Correlation

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 Cumulative Value Added Monthly Cumulative Return Upper 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 *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) *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 Practice
Source: Standard Life Investments

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

47 portable alpha strategies, which is another way of referring to LDI
.. 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 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 ‘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 LDI is about establishing a transparent link between liabilities and assets and minimising uncompensated risks. Hugh Cutler, Pensions Management, 01/04/05 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 focuses on managing a plan’s liability risk while providing multiple sources of excess return. Jane Tisdale, SsgA, 17/10/05 “LDI … the process whereby an investment strategy is set with explicit reference to a specific set of liabilities.” Mercer Investment Consulting

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

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