# 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.

<!--
Guidance: How to use this template
- An SAA study answers one question: which long-term mix of asset
  classes best meets the fund's objectives within its risk limits?
  Most of the variation in a diversified fund's returns over time comes
  from this choice (Brinson, Hood and Beebower, 1986), so treat it as the
  most important investment decision the board makes.
- Run a full study every [three to five] years and after any major
  change in liabilities, funding level, sponsor strength or law. Run a
  light refresh (new assumptions, same method) each year.
- Take the objectives and risk limits from the Investment Policy
  Statement (Institutional) (`finance/investment-policy-statement-institutional`)
  and feed the recommendation back into its section 7. For a private
  client, the equivalent step sits inside Investment Policy Statement
  (Individual) (`finance/investment-policy-statement-individual`).
- Record the decision with an Investment Committee Memo
  (`finance/investment-committee-memo`). Treat the optimiser and the
  asset-liability model as models under the Model Risk Management
  Policy (`finance/model-risk-management-policy`).
- The worked example is fictional: the Northwold Retirement Scheme
  (closed defined benefit, sponsored by Northwold Industries, a fictional
  large employer; base currency EUR, assets EUR 1,080m, liabilities
  EUR 1,200m), with the study prepared by Kestrel Bay Capital Europe as
  OCIO. Assumptions are illustrative and are not
  forecasts.
- Common pitfall: presenting a single "optimal" portfolio as if the
  inputs were known. Expected returns are uncertain; show the range of
  sensible portfolios and why the recommendation is robust.
-->

## Document control

<!-- Guidance: Identify the data date of every input (assets, liabilities, assumptions). A study whose inputs come from different dates must say so. -->

| Field | Value |
|---|---|
| Document ID | [SAA-001] |
| Fund or plan | [Fund name] |
| Prepared by | [OCIO, consultant or internal team] |
| Prepared for | [Investment committee and board] |
| Asset data as at | YYYY-MM-DD |
| Liability data as at | YYYY-MM-DD ([actuarial valuation or roll-forward]) |
| Capital market assumptions as at | YYYY-MM-DD ([provider or internal]) |
| Projection horizon | [10] years, annual steps |
| Base currency | [EUR] |
| Version | [1.0] |
| Status | [Draft / Final / Adopted / Superseded] |
| Owner | [@chief-investment-officer] |
| Approvers/reviewers | [@investment-committee-chair], [@actuary], [@risk-officer], [@independent-adviser] |
| Last updated | YYYY-MM-DD |
| Classification | [Confidential] |

### Approvals

| Role | Name | Decision | Date |
|---|---|---|---|
| Study author | [Name] | [Prepared] | YYYY-MM-DD |
| Independent reviewer | [Name] | [Reviewed, comments addressed] | YYYY-MM-DD |
| Investment committee | [Chair] | [Recommended portfolio B to the board] | YYYY-MM-DD |
| Board | [Chair] | [Adopted] | YYYY-MM-DD |

### Revision history

| Version | Date | Author | Change |
|---|---|---|---|
| 0.1 | YYYY-MM-DD | [@author] | Assumptions and candidate portfolios for committee workshop |
| 0.2 | YYYY-MM-DD | [@author] | Added stress tests and liquidity analysis after workshop |
| 1.0 | YYYY-MM-DD | [@author] | Final recommendation |

## Contents

- [1. Executive summary and recommendation](#1-executive-summary-and-recommendation)
- [2. Scope and questions](#2-scope-and-questions)
- [3. Fund profile and liabilities](#3-fund-profile-and-liabilities)
- [4. Objectives and risk limits](#4-objectives-and-risk-limits)
- [5. Methodology](#5-methodology)
- [6. Capital market assumptions](#6-capital-market-assumptions)
- [7. Candidate portfolios and efficient frontier](#7-candidate-portfolios-and-efficient-frontier)
- [8. Asset-liability and surplus analysis](#8-asset-liability-and-surplus-analysis)
- [9. Stress tests and scenarios](#9-stress-tests-and-scenarios)
- [10. Liquidity and implementation](#10-liquidity-and-implementation)
- [11. Sensitivity and robustness](#11-sensitivity-and-robustness)
- [12. Recommendation and decision record](#12-recommendation-and-decision-record)
- [13. Limitations and model risk](#13-limitations-and-model-risk)
- [14. Glossary and references](#14-glossary-and-references)
- [Standard and licence note](#standard-and-licence-note)

## 1. Executive summary and recommendation

<!-- Guidance: Write this last. One page: the recommendation, why, what changes from today, the main risks and what the board is asked to decide. Pitfall: burying the trade-off the board is actually choosing between. -->

**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).

| Measure | Current A | Recommended B | Change |
|---|---|---|---|
| Expected arithmetic return *(Example)* | 5.45% | 5.42% | minus 0.03 pp |
| Expected volatility *(Example)* | 9.25% | 8.84% | minus 0.41 pp |
| One-year 95% surplus VaR *(Example)* | EUR 211m | EUR 197m | minus EUR 14m |
| Interest-rate hedge ratio *(Example)* | 25.3% | 32.6% | plus 7.3 pp |
| Illiquid assets *(Example)* | 10% | 20% | Plus 10 pp |

## 2. Scope and questions

<!-- Guidance: List the questions the board asked and what is fixed. Pitfall: an unbounded study that optimises everything at once and recommends nothing usable. -->

**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

<!-- Guidance: Describe what the assets must pay for. For a pension fund, use actuarial cash flows; for an endowment, the spending rule; for an insurer, the liability profile by line. Pitfall: using the accounting discount rate when the funding basis drives decisions. -->

| Item | Value *(Example)* | Source |
|---|---|---|
| Market value of assets | EUR 1,080m | Custodian, YYYY-12-31 |
| Present value of liabilities (funding basis) | EUR 1,200m | Actuarial roll-forward |
| Funded ratio | 90.0% (1,080 / 1,200) | Calculated |
| Deficit | EUR 120m | Calculated |
| Liability discount rate | 3.20% | Funding basis |
| Liability modified duration | 16 years | Actuary |
| Liability PV01 | EUR 1.92m (1,200m x 16 x 0.01%) | Calculated |
| Share of liabilities linked to inflation | [60]% | Actuary |
| Net benefit outflow, next 12 months | EUR [32]m (about 3% of assets) | Actuary |
| Sponsor contributions | EUR [15]m a year under recovery plan | [RP-001] |
| Membership | Closed to new entrants since [year]; [x]% pensioners | Administrator |

```mermaid
flowchart LR
  LIAB["Liability cash flows"] --> PV["Present value and PV01"]
  ASSETS["Current assets"] --> FR["Funded ratio and surplus"]
  PV --> FR
  CMA["Capital market assumptions"] --> OPT["Optimiser and ALM model"]
  FR --> OPT
  LIM["IPS risk limits"] --> OPT
  OPT --> CAND["Candidate portfolios"]
  CAND --> STR["Stress tests"]
  STR --> REC["Recommendation"]
```

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

<!-- Guidance: Restate, do not reinvent. The study tests portfolios against the IPS; if the study suggests the IPS limits are wrong, say so in section 12 as a separate recommendation. -->

| ID | From IPS | Test applied in this study |
|---|---|---|
| OBJ-001 *(Example)* | Net return target 4.20% a year (liability rate 3.20% plus 1.00%) | Expected geometric return minus fees at least 4.20% |
| RB-001 *(Example)* | Expected asset volatility not above 10.0% | Section 7 |
| RB-002 *(Example)* | One-year 95% surplus VaR not above EUR 200m | Section 8 |
| RB-003 *(Example)* | Funded ratio floor 80.0% | Section 9 stress tests |
| RB-004 *(Example)* | Interest-rate hedge ratio 30% to 50% | Section 8 |
| RB-005 *(Example)* | Liquidity coverage at least 3.0 times | Section 10 |
| CON-001 *(Example)* | Illiquid assets at most 25% | Optimiser constraint |

## 5. Methodology

<!-- Guidance: Explain the method in words a trustee can follow, then give the formulas. State every simplification. Pitfall: an optimiser left unconstrained, which piles into the asset with the highest assumed return. -->

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 $\rho_{ij}$:

$$
\mu_P = \sum_{i} w_i\,\mu_i, \qquad \sigma_P^2 = \sum_{i}\sum_{j} w_i\,w_j\,\rho_{ij}\,\sigma_i\,\sigma_j
$$

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

$$
g_P \approx \mu_P - \frac{\sigma_P^2}{2}, \qquad S_P = \frac{\mu_P - r_f}{\sigma_P}
$$

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

<!-- Guidance: Show how each assumption was built (building blocks such as cash rate plus risk premium, yield plus roll-down minus defaults, earnings growth plus yield plus valuation change) and its source. Pitfall: mixing arithmetic and geometric returns, or nominal and real, in the same table. -->

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%.

| Code | Asset class | Expected return | Volatility | Building blocks *(Example)* |
|---|---|---|---|---|
| GEQ *(Example)* | Global equity | 7.0% | 16% | Dividend yield plus real earnings growth plus inflation, no valuation change |
| PEQ *(Example)* | Private equity | 9.0% | 24% | Global equity plus illiquidity and leverage premium; volatility de-smoothed |
| RAS *(Example)* | Real assets and infrastructure | 6.0% | 12% | Cash yield plus inflation-linked growth; volatility de-smoothed |
| IGC *(Example)* | Investment grade credit | 4.0% | 6% | Yield to maturity minus expected default losses plus roll-down |
| LGB *(Example)* | Long government bonds | 3.0% | 9% | Current long yield; high volatility from long duration |
| CSH *(Example)* | Cash | 2.0% | 1% | Policy rate path |

**Correlation matrix** *(Example)*:

| | GEQ | PEQ | RAS | IGC | LGB | CSH |
|---|---|---|---|---|---|---|
| GEQ | 1.00 | 0.75 | 0.55 | 0.35 | -0.10 | 0.00 |
| PEQ | 0.75 | 1.00 | 0.50 | 0.30 | -0.10 | 0.00 |
| RAS | 0.55 | 0.50 | 1.00 | 0.35 | 0.10 | 0.00 |
| IGC | 0.35 | 0.30 | 0.35 | 1.00 | 0.55 | 0.10 |
| LGB | -0.10 | -0.10 | 0.10 | 0.55 | 1.00 | 0.10 |
| CSH | 0.00 | 0.00 | 0.00 | 0.10 | 0.10 | 1.00 |

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

<!-- Guidance: Show the current portfolio, the recommended one and two or three alternatives that span the sensible range. Every weight column must sum to 100%. Pitfall: candidates that differ in many ways at once, so the board cannot see what drives the result. -->

| Asset class | A Current | B Recommended | C Higher hedge | D Growth |
|---|---|---|---|---|
| Global equity *(Example)* | 45% | 35% | 28% | 50% |
| Private equity *(Example)* | 5% | 8% | 6% | 10% |
| Real assets *(Example)* | 5% | 12% | 10% | 12% |
| Investment grade credit *(Example)* | 25% | 20% | 22% | 13% |
| Long government bonds *(Example)* | 15% | 23% | 32% | 13% |
| Cash *(Example)* | 5% | 2% | 2% | 2% |
| **Total** | **100%** | **100%** | **100%** | **100%** |

| Metric (before fees) | A | B | C | D |
|---|---|---|---|---|
| Expected arithmetic return *(Example)* | 5.45% | 5.42% | 4.98% | 6.07% |
| Expected volatility *(Example)* | 9.25% | 8.84% | 7.67% | 11.13% |
| Approximate geometric return *(Example)* | 5.02% | 5.03% | 4.69% | 5.45% |
| Sharpe ratio (cash 2.00%) *(Example)* | 0.37 | 0.39 | 0.39 | 0.37 |
| Geometric return minus fees of 0.45% *(Example)* | 4.57% | 4.58% | 4.24% | 5.00% |
| Probability that 10-year annualised return is below 4.20% before fees *(Example)* | 39% | 38% | 42% | 36% |
| Meets RB-001 (volatility at most 10%) | Yes | Yes | Yes | No |

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

<!-- Guidance: Measure risk against the liabilities, not only in asset terms. A portfolio that looks safe in asset terms (cash) can be risky against long-duration liabilities. Pitfall: ignoring the correlation between long bonds and the liability value. -->

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

$$
R_S = R_A - \frac{L}{A}\,r_L
$$

$$
\sigma_S^2 = \sigma_A^2 + \left(\frac{L}{A}\right)^2 \sigma_L^2 - 2\,\frac{L}{A}\,\operatorname{Cov}(R_A, r_L)
$$

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.

| Metric | A | B | C | D |
|---|---|---|---|---|
| Expected surplus return (% of assets) *(Example)* | 1.89% | 1.86% | 1.42% | 2.51% |
| Surplus volatility *(Example)* | 13.04% | 12.22% | 10.53% | 14.90% |
| One-year 95% surplus VaR, EUR *(Example)* | 211m | 197m | 172m | 238m |
| Meets RB-002 (at most EUR 200m) | No | Yes | Yes | No |
| Interest-rate hedge ratio (PV01 basis) *(Example)* | 25.3% | 32.6% | 43.4% | 19.0% |
| Meets RB-004 (30% to 50%) | No | Yes | Yes | No |

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

<!-- Guidance: Apply instantaneous shocks to both assets and liabilities and report the funded ratio after the shock. Include at least one historical-style and one forward-looking scenario relevant to the fund. Pitfall: shocking assets only, which hides the liability hedge benefit or cost. -->

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

| Scenario | GEQ | PEQ | RAS | IGC | LGB | CSH | Liabilities |
|---|---|---|---|---|---|---|---|
| S1 Equity crash, rates fall *(Example)* | -30% | -20% | -10% | -3% | +15% | 0% | +12% |
| S2 Inflation shock, rates rise *(Example)* | -15% | -10% | +2% | -8% | -18% | +1% | -15% |
| S3 Stagflation *(Example)* | -20% | -15% | -5% | -6% | -8% | +1% | -6% |

| Result for B | S1 | S2 | S3 |
|---|---|---|---|
| Asset return *(Example)* | -10.45% | -11.53% | -11.82% |
| Assets after shock (EUR m) *(Example)* | 967.1 | 955.5 | 952.3 |
| Liabilities after shock (EUR m) *(Example)* | 1,344.0 | 1,020.0 | 1,128.0 |
| Funded ratio after shock *(Example)* | 72.0% | 93.7% | 84.4% |
| Breaches RB-003 floor of 80.0%? | Yes | No | No |

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%.

| Funded ratio after S1 *(Example)* | A | B | C | D |
|---|---|---|---|---|
| Asset return | -13.50% | -10.45% | -6.46% | -16.64% |
| Assets after shock (EUR m) | 934.2 | 967.1 | 1,010.2 | 900.3 |
| Funded ratio (liabilities EUR 1,344.0m) | 69.5% | 72.0% | 75.2% | 67.0% |

**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

<!-- Guidance: Test whether the fund can pay benefits and collateral calls in stress without selling illiquid assets at a discount, and plan the move from today's portfolio. Pitfall: ignoring the J-curve and commitment pacing for private assets. -->

| Liquidity test for B *(Example)* | Value |
|---|---|
| Assets with daily or weekly liquidity | GEQ 35% + IGC 20% + LGB 23% + CSH 2% = 80% (EUR 864.0m) |
| Stressed haircut on liquid assets (S1) | Assume 15%, leaving EUR 734.4m |
| Next 12 months net outflows plus collateral call in a 1 pp rate rise | EUR 32m + EUR 40m = EUR 72m |
| Liquidity coverage | 734.4 / 72 = 10.2 times (limit at least 3.0) |

**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.**

| Step | Trade *(Example)* | Timing | Estimated cost |
|---|---|---|---|
| T1 | Sell 10 pp global equity (EUR 108.0m) | Quarter 1 | [0.10]% |
| T2 | Buy 8 pp long government bonds (EUR 86.4m) | Quarter 1 | [0.05]% |
| T3 | Reduce cash by 3 pp and credit by 5 pp (EUR 86.4m) to meet real asset calls (7 pp) and part of private equity calls (1 pp) | Quarters 1 to 8 | [Manager fees] |
| T4 | Commit to private equity programme; the 2 pp left from T1 after T2 funds the rest of the 3 pp increase | Annual | [Fees] |

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

## 11. Sensitivity and robustness

<!-- Guidance: Show what happens when the most uncertain assumptions change. A recommendation that flips with a small change in one input is not robust. -->

| Sensitivity | Effect on B *(Example)* | Still meets target and limits? |
|---|---|---|
| Global equity expected return 1 pp lower (6.0%) | Expected return 5.42% minus 0.35 = 5.07%; geometric about 4.68% | Yes, margin after fees narrows to about 0.03 pp |
| All correlations raised by 0.2 (crisis) | Volatility and surplus VaR rise; rerun [results] | [Result] |
| Liability discount rate 0.5 pp lower | Liabilities rise by about 8% (16 x 0.5%) to about EUR 1,296m; funded ratio about 83% before the gain on hedging assets | Recovery plan review |
| Private asset volatility de-smoothed further (+5 pp) | Optimiser lowers PEQ and RAS weights | [Result] |
| Inflation 1 pp higher for five years | Real assets and inflation-linked liabilities move together; real return target harder | [Result] |

**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

<!-- Guidance: Turn the analysis into exact IPS changes: targets, ranges, benchmarks, hedge ratio and implementation timing. Pitfall: recommending targets without ranges, so the IPS cannot be monitored. -->

| Asset class | Recommended target | Range | Benchmark to set in IPS |
|---|---|---|---|
| Global equity *(Example)* | 35% | 30% to 40% | [Global equity index, net, 50% EUR hedged] |
| Private equity *(Example)* | 8% | 4% to 12% | [Public equity plus 3%, lagged] |
| Real assets *(Example)* | 12% | 8% to 16% | [Infrastructure or property index] |
| Investment grade credit *(Example)* | 20% | 15% to 25% | [EUR corporate index] |
| Long government bonds *(Example)* | 23% | 18% to 30% | [Liability benchmark] |
| Cash *(Example)* | 2% | 0% to 5% | [EUR short-term rate] |
| **Total** | **100%** | | |

**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.]

| Decision ID | Date | Body | Decision | Vote | Minute ref |
|---|---|---|---|---|---|
| DEC-001 *(Example)* | YYYY-MM-DD | Investment committee | Recommend B to board | 5 for, 0 against | [IC-YYYY-07] |
| DEC-002 *(Example)* | YYYY-MM-DD | Board | Adopt B; update IPS section 7 | Unanimous | [BRD-YYYY-09] |

## 13. Limitations and model risk

<!-- Guidance: Be explicit about what the study cannot tell the board. Regulators and auditors look for this section. -->

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

<!-- Guidance: Define terms used in the study and list the sources of every assumption with their dates. -->

### 14.1 Glossary

| Term | Meaning |
|---|---|
| Capital market assumptions | Forward-looking expected returns, volatilities and correlations for asset classes |
| De-smoothing | Adjusting appraisal-based returns to estimate true economic volatility |
| Efficient frontier | Set of portfolios with the highest expected return for each level of risk |
| Geometric return | Compound average growth rate; below the arithmetic mean when returns vary |
| PV01 | Change in present value for a 0.01 percentage point change in rates |
| Sharpe ratio | Expected excess return over cash per unit of volatility |
| Surplus risk | Volatility of assets minus liabilities, scaled to assets |
| Value at risk (VaR) | Loss not expected to be exceeded at a stated confidence over a stated period |

### 14.2 References

| Reference | Use |
|---|---|
| Markowitz, H. (1952), Portfolio Selection, Journal of Finance 7(1) | Mean-variance method (idea cited) |
| Sharpe, W. F. (1966), Mutual Fund Performance, Journal of Business 39(1, Part 2) | Sharpe ratio |
| Black, F. and Litterman, R. (1992), Global Portfolio Optimization, Financial Analysts Journal 48(5) | Alternative method |
| Brinson, G. P., Hood, L. R. and Beebower, G. L. (1986), Determinants of Portfolio Performance, Financial Analysts Journal 42(4) | Importance of policy allocation |
| Directive (EU) 2016/2341 (IORP II), Articles 19, 25 and 30 | SAA in statement of investment policy principles; ALM in risk management |
| Occupational Pension Schemes (Investment) Regulations 2005, regulation 2 | Kinds and balance of investments, risks, expected return in the UK SIP |
| [External capital market assumptions, provider and date] | Comparison of assumptions |
| Related templates | `finance/investment-policy-statement-institutional`, `finance/investment-committee-memo`, `finance/model-risk-management-policy`, `finance/stress-testing-programme` |

## 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.