Strategic Asset Allocation and ALM Study Template

Strategic Asset Allocation Study: [Fund or plan name]

The analysis behind a fund's long-term policy portfolio: fund profile and liabilities, objectives and risk limits taken from the investment policy statement, capital market assumptions, candidate portfolios and the efficient frontier, asset-liability and surplus analysis, stress tests, liquidity and implementation, sensitivity checks and a recommendation the board can adopt. Uses standard published methods (mean-variance optimisation after Markowitz, 1952; surplus optimisation; scenario analysis) and supports the strategic asset allocation content that Directive (EU) 2016/2341 (IORP II) Article 30 and the UK Occupational Pension Schemes (Investment) Regulations 2005 regulation 2 expect in a statement of investment principles, as in force in October 2026. Replace every [bracketed] value and every row marked Example. This is a document format, not financial, investment, tax, legal or accounting advice. Obtain the standard or law for the normative text.

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Contents

• 1. Executive summary and recommendation
• 2. Scope and questions
• 3. Fund profile and liabilities
• 4. Objectives and risk limits
• 5. Methodology
• 6. Capital market assumptions
• 7. Candidate portfolios and efficient frontier
• 8. Asset-liability and surplus analysis
• 9. Stress tests and scenarios
• 10. Liquidity and implementation
• 11. Sensitivity and robustness
• 12. Recommendation and decision record
• 13. Limitations and model risk
• 14. Glossary and references
• Standard and licence note

1. Executive summary and recommendation

Decision requested. [Adopt portfolio B as the new policy portfolio and the ranges in section 12, effective YYYY-MM-DD, with transition over [two] quarters.]

Key findings (Example):

1. The current portfolio A meets the 4.20% net return target on expected values but breaches the surplus value at risk limit (EUR 211m against a limit of EUR 200m).
2. Portfolio B keeps almost the same expected return (5.42% against 5.45%), lowers asset volatility from 9.25% to 8.84% and surplus value at risk to EUR 197m, inside the limit, by moving 18 percentage points out of global equity (10), credit (5) and cash (3) into long government bonds (8), real assets (7) and private equity (3).
3. Portfolio C lowers surplus risk further (EUR 172m) but its expected geometric return of 4.69% leaves little margin over the target after fees of about 0.45%.
4. Portfolio D has the highest expected return but the highest surplus risk (EUR 238m) and is outside the limit.
5. The recommendation holds when equity expected returns are 1 percentage point lower (section 11).

2. Scope and questions

Questions the study answers.

1. Is the current policy portfolio still consistent with the return target and risk limits in the IPS?
2. Which mix of the permitted asset classes gives the best expected return for the surplus risk the board accepts?
3. How much interest-rate and inflation hedging is appropriate?
4. Can the fund afford more illiquid assets given benefit outflows and collateral needs?

Fixed for this study. Permitted asset classes and prohibited instruments (IPS section 11); the funding plan and contribution schedule [RP-001]; the end-game target [low-dependency funding by year].

Out of scope. Manager selection, currency hedge ratios inside asset classes (reviewed separately in [study]) and the responsible investment policy, except as constraints.

3. Fund profile and liabilities

Describe the expected path of liability cash flows (a chart or table by year), the point at which outflows exceed contributions, and any events that change the profile (buy-ins, bulk transfers, benefit changes).

4. Objectives and risk limits

5. Methodology

The study combines four steps:

1. Capital market assumptions for each asset class: expected return, volatility and correlations over the horizon (section 6).
2. Mean-variance optimisation to find efficient portfolios, with minimum and maximum weights so results stay investable.
3. Surplus analysis that measures risk relative to the liabilities, because the fund's purpose is to pay them (section 8).
4. Scenario and stress tests that do not depend on normal distributions (section 9).

For a portfolio with weights $w_i$, expected returns $\mu_i$, volatilities $\sigma_i$ and correlations $\rho_{ij}$:

The approximate long-run compound (geometric) return and the Sharpe ratio with risk-free rate $r_f$ are:

The optimiser solves, for each target level of risk, the portfolio with the highest expected return subject to: weights sum to 100%; each weight within its minimum and maximum; illiquid assets at most 25%; and no short positions. Repeating this for many risk levels traces the efficient frontier.

Alternatives considered. [Resampled efficient frontier; a Black-Litterman approach that starts from market-capitalisation weights and blends in views; risk-parity weights; a full stochastic asset-liability model with 5,000 economic scenarios.] State which were used and why. In the example, mean-variance with constraints is the main tool and a stochastic model run by the actuary confirms the funding-level results.

6. Capital market assumptions

Conventions: nominal, EUR base, annual, arithmetic expected returns, before fees, horizon [10] years, foreign assets hedged to EUR as stated. Inflation assumption 2.00%, cash (risk-free) rate 2.00%.

Correlation matrix (Example):

Check that the matrix is positive definite before use (all eigenvalues above zero); the example matrix passes.

Private asset volatility. Reported returns of private equity and real assets are smoothed by appraisal-based valuation. Use de-smoothed volatility (for example, by an autocorrelation adjustment) or the study will overweight them.

Source and comparison. Compare the assumptions with at least [two] external sets (published long-term assumptions from consultants or managers, name and date only) and explain differences above [1] percentage point.

7. Candidate portfolios and efficient frontier

Worked check for B: expected return = 0.35 x 7.0 + 0.08 x 9.0 + 0.12 x 6.0 + 0.20 x 4.0

• 0.23 x 3.0 + 0.02 x 2.0 = 2.45 + 0.72 + 0.72 + 0.80 + 0.69 + 0.04 = 5.42%. Geometric approximation = 5.42% minus 8.84% squared over 2 (0.39%) = 5.03%. The shortfall probability uses a normal approximation for the annualised 10-year compound return with mean equal to the geometric return and standard deviation equal to volatility divided by the square root of 10.

Frontier. Plot expected return against volatility (and separately against surplus volatility) for the constrained frontier, and mark A, B, C and D. In the example, A sits below the frontier: B gives almost the same expected return with lower risk because it diversifies into real assets and private equity and replaces cash with long government bonds.

8. Asset-liability and surplus analysis

The surplus return relative to assets $A$, with liabilities $L$ and liability return $r_L$, is:

Liability assumptions (Example): expected liability return 3.20% (the discount rate), liability volatility 10%, correlation with asset classes GEQ -0.05, PEQ -0.05, RAS 0.10, IGC 0.60, LGB 0.90, CSH 0.05. L/A = 1,200 / 1,080 = 1.111.

Surplus VaR = (1.645 x surplus volatility minus expected surplus return) x EUR 1,080m. For B: (1.645 x 12.22% minus 1.86%) x 1,080 = (20.10% minus 1.86%) x 1,080, about EUR 197m.

Hedge ratio. For B, long government bonds of EUR 248.4m with modified duration 20 give PV01 of EUR 0.50m and credit of EUR 216.0m with duration 6 gives EUR 0.13m. Together EUR 0.63m against liability PV01 of EUR 1.92m is a hedge ratio of about 32.6%, inside the 30% to 50% range. The same calculation gives 25.3% for A (EUR 0.49m), 43.4% for C (EUR 0.83m) and 19.0% for D (EUR 0.37m), so A and D also fail RB-004. Only B and C pass every limit in section 4, and B is preferred because its expected return after fees leaves a margin of 0.38 percentage points over the 4.20% target, against 0.04 points for C. Raising the hedge ratio further (portfolio C) cuts surplus risk but costs expected return, unless done with a derivatives overlay, which the committee may consider under IPS section 11.

9. Stress tests and scenarios

Shocks are instantaneous and applied to portfolio B (EUR 1,080m assets, EUR 1,200m liabilities).

Worked check for S1: (0.35 x -30) + (0.08 x -20) + (0.12 x -10) + (0.20 x -3) + (0.23 x 15) + (0.02 x 0) = -10.50 - 1.60 - 1.20 - 0.60 + 3.45 + 0 = -10.45%. Assets 1,080 x (1 - 0.1045) = 967.1; liabilities 1,200 x 1.12 = 1,344.0; funded ratio 967.1 / 1,344.0 = 72.0%. The shocks are internally consistent: a liability rise of 12% at duration 16 implies a fall in long rates of about 0.75 percentage points, which lifts long government bonds of duration 20 by about 15%.

Interpretation. S1 breaches the funded ratio floor for every candidate (69.5% to 75.2%) because every hedge ratio is below one half; the board should note this as an accepted risk or ask for the overlay option. Record the response in the contingency plan agreed with the sponsor.

10. Liquidity and implementation

Private asset pacing. To move private equity from 5% to 8% and real assets from 5% to 12%, commit EUR [x]m a year for [three] years, using expected call and distribution rates. Show projected net asset value as a share of assets each year so the 25% illiquid ceiling holds under the S1 shock (the denominator effect).

Transition plan.

Use a transition manager for physical moves above EUR [50]m and record pre-trade estimates and post-trade results.

11. Sensitivity and robustness

Robustness checks. [Resampling of inputs gives an average weight for B within plus or minus [3] pp of the recommendation; a Black-Litterman run from market weights gives [result].] Prefer a portfolio that sits in the middle of the efficient region rather than at a corner.

12. Recommendation and decision record

Other recommendations. [Keep RB-002 at EUR 200m; consider a rates overlay to lift the hedge ratio to 40% at the next review; review the assumptions annually.]

13. Limitations and model risk

1. Assumption uncertainty. Expected returns are estimates with wide error bands; a small change can move optimal weights a lot.
2. Distribution shape. Mean-variance and parametric VaR assume normal returns; real markets show fat tails and rising correlations in crises. Stress tests in section 9 partly address this.
3. Single-period view. The optimiser is single-period; contributions, benefit payments and rebalancing over time are approximated.
4. Private asset data. Appraisal-based returns understate risk.
5. Liability model. Liability cash flows depend on demographic assumptions that the actuary reviews separately.
6. Model governance. The optimiser and asset-liability model are validated under the fund's model risk policy; results are reproducible from the stored input file [path].

14. Glossary and references

14.1 Glossary

14.2 References

Standard and licence note

Based on published academic methods (mean-variance optimisation, surplus analysis, the Sharpe ratio), which are ideas expressed here in original formulas and words with citation, and on Directive (EU) 2016/2341 Articles 19, 25 and 30 (EUR-Lex, reuse with acknowledgement) and the UK Occupational Pension Schemes (Investment) Regulations 2005 regulation 2, cited by number only. No text, charts or figures are copied from books, journal articles, consultants' publications or capital market assumption reports; all example figures are fictional and computed for this template. The structure and text are original. Check the current official versions of the cited laws and your national rules before use. This is a document format, not financial, investment, tax, legal or accounting advice. Template licence: MIT.