A. Managing Liquidity Risk

Financial institutions must maintain sufficient liquidity to meet obligations at all times.

• Banks: Depositor withdrawals
• Insurance companies: Policy claims (death, accidents, etc.)

Endowment Fund Example (ABC Endowment supporting ABC University)

Cash Inflows (c/i): Donations + Investment returns
Cash Outflows (c/o):

• Distributions to university (spending requirements)
• Capital calls on PE fund commitments

During Crisis — Liquidity Crunch Dynamics

• ==Contributions ↓ (donors face hardships)==
• ==Capital calls ↑ (PE managers buy distressed assets)==
• ==Investment returns ↓ (capital losses mount)==
• ==Spending ↑ (university revenues fall → needs more support)==

Result: c/i ↓↓ and c/o ↑ → Liquidity Crunch. Endowment may need to cut distributions or borrow.

Liquidity Budget (Key Planning Tool)

Categorize assets by time required to convert to cash without significant market impact.

Example Calculation (==assume portfolio value = $100==):
Weighted liquidity available =

• Small Cap: 20 × 15% = 3
• Large Cap: 30 × 45% = 13.5
• Bonds: 50 × 70% = 35

→ $51.5 available in 0-1 month (51.5% of portfolio)
1-3 mo: $21.6 | 3-6 mo: $26.9

Conclusion: This portfolio has sufficient liquidity (51.5% convertible into cash within 1 month without significant market impact).

Tradeoff: More liquid assets → lower expected yield. ==Illiquid assets → higher yield (illiquidity premium) but greater liquidity risk.==

#EXAMPLE

• Analyze and monitor portfolio liquidity characteristics over time; same asset class can have vastly different liquidity profiles
• Liquid allocation: (1% × 100%) + (24% × 100%) + (39% × 50%) = 44.5% (exceeds 30% minimum)
• Illiquid allocation: (21% × 100%) + (15% × 50%) = 28.5% (below 40% maximum)
• Conclusion: Capacity exists to shift funds from liquid to illiquid investments for higher returns; Bookman can recommend this reallocation

B. Managing Illiquid Assets

Illiquid assets (PE, real estate, certain hedge funds) are difficult and costly to rebalance.

1. Calendar-Based Rebalancing

Rebalance at fixed intervals (e.g., quarter-end). ==Reduces transaction costs but allows drift from SAA between dates.==

2. Percentage-Based (Band) Rebalancing

Trigger rebalance when allocation breaches a pre-set band (e.g., small cap target 10–15%). Rebalance to outer bound or midpoint.

Key insight: Narrow bands → frequent rebalancing & high costs. ==Use wider bands for more volatile/illiquid asset classes.==

3. Automatic Adjustment Mechanisms

Instead of trading the illiquid asset directly, adjust a correlated liquid proxy.

Example: If PE exposure exceeds its limit due to appreciation, reduce public equity allocation to keep overall equity risk in check.
Caution: This increases overall portfolio illiquidity (liquidity risk rises).

PE-Specific Note: Capital calls occur over time; distributions come at the end. Investors have limited control during ramp-up and wind-down phases. ==Diversify PE holdings across vintage years to avoid concentration/maturity risk.==

4. Stress Testing

Simulate crisis scenarios (donations ↓, capital calls ↑, returns ↓, spending ↑) to verify the endowment can meet long-term objectives without forced sales at depressed prices.

5. Using Derivatives for Liquidity

Example: Want +$50M bond exposure.

• Buying bonds directly uses full $50M capital.
• ==Long bond futures (margin ≈ $5M) → instant exposure + $45M capital freed== → higher portfolio liquidity.

#EXAMPLE

• Rebalancing band principle: Wider bands for higher-volatility assets; narrower bands for lower-volatility assets (compare σ to range)
• Cash: Lowest σ but widest bands → reduce band width
• Private equity: Highest σ but tightest bands → expand band width

C. Illiquidity Premium

Core Principle: ==The more illiquid the asset and/or the longer the expected illiquidity period, the higher the illiquidity premium (additional return) investors require.==

==Approaches== to Estimating Illiquidity Premium

1. Put Option (Liquidity) Value Approach

A put gives the right to sell → effectively generates liquidity (cash flow).

For equivalent assets differing only in liquidity:

$$ P(\text{illiquid asset}) + \text{Value of Put (liquidity)} = P(\text{liquid asset}) $$

$$ \text{Illiquidity Premium ($)} = P(\text{liquid}) - P(\text{illiquid}) $$ #EXAMPLE

• Illiquidity premium = expected compensation for locking up capital for an uncertain period
• Estimation via put-option model: discount size = value of a put option struck at the hypothetical “marketable price” of the illiquid asset at purchase
• formula: illiquid asset price = marketable price − put price
• If both prices are known/estimated, compute expected return for each; the spread = illiquidity premium (%)

#EXAMPLE

(Bonds identical in interest rate & credit risk, differ only in liquidity):

Bond	Liquidity	Price	Yield
A	High	100	6%
B	Low	103	8%

Illiquidity Premium = ==2% (yield) or $3 (price differential).== (due to liquidity, credit risk, co size)

2. Index-Based Approach

$$ \text{Illiquidity Premium (%)} \approx R(\text{Index of Illiquid Assets}) - R(\text{Index of Liquid Assets}) $$

Limitations:

• I==ndex is a broad average== → may under- or over-state the premium for specific assets.
• Observed spread ==may include other premia (credit risk, size, etc.).==
• Requires ==attribution analysis to isolate the pure liquidity component==.

#EXAMPLE

• Endowment should target longer-term (more illiquid) investments within asset classes to capture highest liquidity premium
• Literature confirms positive illiquidity–return relationship in public equity, but premium is hard to isolate from market, value, and size factors
• Illiquidity premium estimates use broad indexes; actual investors hold small subsets, so experience varies widely and is exposed to idiosyncratic risk
• These challenges underscore need for liquidity budgeting to capture illiquidity premium while controlling risk

D. Asset Allocation in Relation to Liquidity Needs

Crisis Impact on Private Equity Liquidity

• ==Capital calls accelerate: PE managers find distressed opportunities → investors must deploy capital faster than planned.==
• ==Distributions decline: Managers postpone exits to avoid selling at distressed prices.==
• ==Valuation smoothing/lag: PE appraised values (based on historical comps) lag public markets. In a crisis, public prices fall but PE values may appear overstated → reported exposure looks inflated while true liquidity is worse.==

Recommendation: Apply unsmoothing techniques to obtain realistic volatility and correlation estimates.

#EXAMPLE #explain

The design of the spending rate policy incorporates a ==smoothing, counter-cyclical element,== leading to ==spending rates below 5% in a period of sustained strong investment returns but higher than 5% in a protracted weak return environment.== This design of the spending rate policy ==exacerbates the endowment’s liquidity needs in severe market downturns.==

Illustrative Liquidity Pressure Example

Endowment distributes fixed $5M/year.

• Normal: $100M PF → liquidity need = 5%
• Crisis: PF drops to $70M → liquidity ==need rises to ≈7.1% ($5M / $70M)==

Problem: Liquidity requirement increases precisely when assets are becoming more illiquid.

==Tools== for Managing Liquidity Risk (Institutional)

1. Cash Flow Forecasting - ==forecasting models and commitment-pacing models==

   • Outflows: Forecast spending + capital call drawdowns. Diversify PE across vintage years.
   • Inflows: Donations (highly procyclical — fall sharply in crises).

2. Liquidity Budget — Map % of portfolio by conversion horizon (see 30.1).

3. Stress Testing — Ensure ability to meet commitments without liquidating high-risk assets at fire-sale prices.

#EXAMPLE

Project endowment distributions to the university as cash outflows, governed by a ~5% annual spending rate policy that aims to preserve purchasing power.

• In stress periods, distributions from prior private investments may cease (GPs struggle to exit), while future capital commitments remain legal obligations; GPs may accelerate calls in depressed markets.
• Diversify across fund vintage years to avoid cycle concentration; commit capital steadily to limit large allocation shifts and transaction costs.
• Avoid outsized allocations to few funds to reduce idiosyncratic risk.
• Donor inflows typically drop sharply in stress, raising liquidity needs; liquidating risk/high‑beta assets after losses is valuation‑unfriendly.
• Access to top private‑market managers is competitive and may be unavailable when the portfolio seeks to increase allocations.
• Develop a liquidity budget setting minimum liquidity targets based on conversion timeframes; monitor it regularly.
• Run regular stress tests (historical & hypothetical scenarios) to gauge liquidity‑profile drift and verify the minimum budget is met.

E. Asset Allocation in Relation to Risk/Return (Endowments)

Typical Endowment Objectives

1. Meet current spending requirements of beneficiary → Real return component (e.g., 5%)
2. Maintain long-term purchasing power → Inflation component (e.g., 2%)

$$ \text{Required Nominal Return} \approx r_{\text{real spending}} + \pi $$

Spending Policy (Smoothed)

Annual spending is usually a weighted average of:

• Last year’s actual distribution (S_{t-1})
• Long-term policy rate = (Long-term real geometric average return × Beginning VPF) <–==note beginning of period==

Example (Year ended 2020):
Spending (S) = $30M | Year-end VPF (2020, = beg 2021) = $500M | L.T. real geo avg return = 5%

$$ S(2021) = \frac{2}{3} \times $30\text{M} + \frac{1}{3} \times (5% \times $500\text{M}) = $20\text{M} + $8.333\text{M} = $28.333\text{M} $$

Why smooth? Pure % of current VPF transmits portfolio volatility directly into spending uncertainty. Smoothed policy stabilizes cash flows to the university.

Key Implications for Endowment Asset Allocation

• ==Liquidity requirements are relatively low== (≈5.7% of VPF in example) → can ==allocate more to illiquid assets== (PE, HF, RE) to capture the illiquidity premium.
• ==Perpetual horizon== (no maturity) supports higher allocation to illiquid/higher-risk assets.
• Tolerating more short-term risk can ==produce higher long-term returns== → greater sustainability.

#EXAMPLE

• Spending rate policy uses counter-cyclical smoothing: rates below 5% in strong return periods, above 5% in weak return periods
• This design exacerbates endowment liquidity needs during severe market downturns
• Smoothed policy stabilizes university cash flows, provides predictable distribution pattern, enables better resource deployment planning

#EXAMPLE

How to address downturn/stress concerns?

• Ensure proposed allocation complies with liquidity budgeting framework under stress conditions
• Grides to closely monitor portfolio liquidity profile and conduct periodic stress tests, especially near minimum thresholds
• Address risk-profile “drift”: illiquid assets carry high rebalancing costs; liquidity decreases in market stress
• Maintain sufficient liquid assets and rebalancing mechanisms to keep risk profile within targets
• Proposed allocation reduces liquid assets → Must adopt effective rebalancing mechanism before investing and follow consistently
• Rebalancing options:
  • Systematic: calendar or percent-range rebalancing with pre-set tolerance bands
  • Automatic: adjust correlated public-market allocation to rebalance private-market (illiquid) risk

#EXAMPLE

Reasons to justify the increased risk profile include the following:

• Board members’ ==lower return expectations for public equity and fixed-income asset classes== imply higher risk required to achieve same returns
• Long-horizon investor illiquidity tolerance enables: improved diversification, higher expected returns, broader strategy access; in MVO, illiquid assets shift efficient frontier upward → greater efficiency (higher returns at all risk levels or higher $Sharpe\ ratio$)
• Endowment risk profile ==currently more conservative vs peer universities==
• Monte Carlo simulations: proposed allocation shows higher probability of meeting return target while better preserving purchasing power

#EXAMPLE

for a long-horizon institutional investor, the ability to tolerate illiquidity creates an opportunity to improve portfolio diversification and expected returns as well as access a broader set of investment strategies. In mean-variance optimization models, the inclusion of illiquid assets in the eligible investment universe may shift the ==efficient frontier upwards,== theoretically resulting in more-efficient investment portfolios (==i.e., portfolios with a higher expected return for a given level of risk==). endowment has been building exposure to these strategies over the past two decades in a gradual manner. reputation as a well-informed, patient, and reliable long-term investo

Tradeoff — PE/HF/RE Holdings
Short-term: ==Higher management fees + illiquidity.==
Long-term: ==Lower probability of capital depletion (more sustainable real spending + inflation protection).==

#EXAMPLE

• Monte Carlo simulations: 70% chance proposed SAA maintains ≥75% purchasing power over 20 years (5% spending rate) vs 65% for current SAA
• Proposed SAA more efficient (higher Sharpe ratio)
• Confirm risk profile matches risk tolerance:
  • Return volatility: 13.2%
  • Purchasing power erosion probability (Exhibit 10)
• Confirm increased illiquid allocation aligns with liquidity budget

#EXAMPLE

• Proposed allocation shifts liquidity profile: reduces highly liquid/liquid categories; increases semi-liquid/illiquid categories across all scenarios, especially under high stress
• High-stress breach of liquidity budget minimums:
  • Highly liquid: 9% (min 10%)
  • Moderately liquid: 19% (min 35%)
  • Illiquid: hits upper limit 50%
• Requirement: allocation must still meet cash-outflow & rebalancing needs even under stress
• Action: reduce exposure to less-liquid commingled funds in public equity to improve extreme-stress liquidity
• Ongoing: monitor liquidity profile closely; run periodic stress tests, particularly when near budget thresholds

F. Issues in Asset Manager Selection

Smaller endowments often hire external managers because in-house operations carry high fixed costs (staff, technology, compliance) and external managers provide access to diverse styles.

Internal focus shifts to: strategic asset allocation, overall risk management, and manager performance evaluation.

Ethical & Governance Considerations (Non-Arm’s Length Relationships)

• Disclose any ==actual or perceived conflicts of interest== (e.g., external manager on university board or major donor).
• Recuse conflicted parties from hiring/monitoring decisions.
• Perform ongoing due diligence: understand strategy and verify performance against mandate. ==Avoid “set-it-and-forget-it”.==

#EXAMPLE

• Code & Standards violations

  • Falsely claims CFA Institute designated Langhorne a “member firm” — membership is individual only
  • Breaches: Standard I(C) Misrepresentation; Standard VII(A) Conduct as Participants; Standard VII(B) Reference to CFA Institute

• GIPS violations (performance reporting)

  • Net-of-fees composite returns permitted
  • Adjusting benchmark returns with a hypothetical fee for comparison is prohibited — gross-of-fees composite must be compared to unadjusted benchmark (Section 4.a.1 Disclosure Requirements)
  • Hypothetical 1.00% fee > actual composite fee (~0.63%)
  • Fee calc: $\frac{(0.0100 \times 5\text{M}) + [0.0060 \times (60\text{M} - 5\text{M})]}{60\text{M}} = 0.0063 = 0.63%$
  • Result: larger benchmark cost creates phantom outperformance

• Further Code & Standards breaches (performance)

  • Standard III(D) Performance Presentation — inaccurate comparison
  • Standard I(D) Misconduct — deceitful presentation (GIPS Fundamentals §0.A.7)

• Independence & Objectivity breach

  • All-expenses-paid golf outing for Foundation CIO & president
  • Standard I(B) Independence and Objectivity — invitation “regardless of outcome” still functions as quid pro quo for future hiring of Langhorne==

G. Integrating ESG into Decision Making

Example: External manager holds Blue Co. (major campus food service provider) facing criticism over employee treatment. University faces dual issues: (i) investment holding and (ii) vendor relationship.

Steps for ESG Integration in Investment Process

1. Pre-hire evaluation: Assess external manager’s formal ESG policy and its consistency with the foundation’s policy.
2. Portfolio-level aggregation: Measure combined carbon footprint, climate sensitivity; compare ESG ratings vs. peers.
3. Continuous monitoring: Track changes in ESG ratings; early action reduces long-term remediation costs.
4. Responsible ownership tools: ==Proxy voting + active engagement.== ==Divestment is a last resort, not the default action.==

#EXAMPLE

For the vendor relationship: The university can switch providers if Blue Co. fails to improve.

H. Derivatives vs. Cash Market Rebalancing

Scenario: Strategic equity allocation = 70%, drifted to 62% after correction → need to increase equity exposure.

Cash Market Rebalancing (Trade Underlying)

• Takes time (selection + execution) → performance drag.
• Accumulates transaction costs + potential market impact (especially large trades).
• Best for permanent or large rebalancing (buy-and-hold).

Derivatives Rebalancing (e.g., Long Equity Index Futures)

• Highly liquid, low transaction costs, no market impact.
• Instant exposure; easily reversed → ideal for temporary rebalancing.
• Requires only margin (capital efficient).
• Limitations: Tracking error if portfolio ≠ index; futures have expiries (roll risk, contango when r > y); generally unavailable for illiquid assets like PE (use cash market for those).

Rule of thumb: Use ==derivatives for temporary/tactical adjustments; cash market for structural changes or illiquid asset classes==.

#EXAMPLE

• Use derivatives instead of cash for rebalancing when: - ==The sell-off is viewed as temporary and external manager performance is satisfactory==. - ==The goal is to rebalance without reallocating among external managers==.
  • High liquidity and flexibility are required to adjust/reverse exposures quickly.
• Advantages of derivatives:
  • Quick implementation.
  • Tactical flexibility.
  • ==Ability to leave external managers in place==.
  • High liquidity.
• Risks:
  • Derivatives introduce tracking error and operational risks, but these are mitigated by the short-term nature of the rebalancing.
• Example:
  • ==Establish a $1.2%$ long position to the S&P 500 Index using short-term S&P 500 futures to rebalance US public equities==.

I. Tactical Asset Allocation (TAA)

TAA = short-term deviations from long-term Strategic Asset Allocation (SAA) to exploit perceived opportunities.

Example:
SAA: Stocks 60% ±5% [55–65%], Bonds 40% ±5% [35–45%].
Manager is bullish on stocks → TAA: Stocks 65%, Bonds 35% (+5% shift from bonds to stocks).

Implementation Tools (Asset-Class Views)

• Long Equity Swap
• Long Equity Futures
• Long Equity Index ETFs

Capital Efficiency Comparison (Illustrative $5M shift)

• Swaps & Futures: No (or minimal) upfront capital beyond margin; remainder earns R_f → high flexibility.
• ETFs: Require full notional capital upfront → least capital efficient; potential cash drag.
• Capital Commitment: Total return swap requires much lower cash outlay than unlevered ETF (full notional); derivatives are cash-efficient for incremental exposure
• Liquidity: ETF more efficient — exchange-traded, standardized, narrow spreads; swap is OTC, negotiated, customizable (maturity, leverage, cost)
• Tracking Error: Swap replicates index exactly (counterparty obligated to deliver index return); ETF has tracking error from NAV premiums/discounts, cash drag, regulatory diversification rules; swap introduces counterparty credit risk and net exposure management complexity

Key Implementation Notes

• ETFs & futures = exchange-traded → more liquid. Swaps = OTC → less liquid + counterparty risk.
• Futures: Daily mark-to-market (margin calls); must roll at expiry (contango risk if F > S_0 ==when carry cost r > yield y==).

• Extracted text This implies that every time a futures is renewed, the Long has to lock in a Buy price that is higher than prevailing Spot Price.

Recall: $F = \frac{S_0(1+r)^t}{(1+y)^t}$

$r$: carry costs (i.e. interest rates) $y$: yield on the underlying $\therefore$ if $(r > y) \Rightarrow (F > S_0)$

• Tracking error: Futures/swaps ≈ 0 to index; ETFs often have residual cash → higher TE.

J. Implementation Costs of TAA

Example: Manager wants to implement +$10M TAA (or rebalancing) from bonds to stocks.

Part I — Unlevered Trade Costs (bps, ==incl. commissions, spreads, roll, market impact==)

Part II — With Leverage (5:1, A/E = 5 → 80% debt / 20% equity)

R_f = 3%, Loan rate = 3.6%.

ETF Levered Cost (per $100 exposure): $80 financed via debt → leverage cost = 3.6% × 80 = $2.88 → 288 bps. (2.88 out of 100 total)

==Futures / Swaps== Levered: Only margin posted (e.g., 20% = $20); earns R_f. Net carry cost lower (≈240 bps) because interest income on margin offsets part of financing cost. (3.00 - 0.60 = 2.40)

Total Cost Summary (bps) — Levered TAA

ETF: (USD5 million × 0.6667 × 2.15%)/USD5 million = 1.43% (or 143 bps) and Futures: (USD5 million × 0.6667 × 1.80%)/USD5 million = 1.20% (or 120 bps), ETF: 27 bps + 143 bps = 170 bps and Futures: 32 bps + 120 bps = 152 bps.

#EXAMPLE

Given:

• Increase allocation to Int’l equities by TSM.

• Unlevered Trade Costs (bps): ETFs 27bps, Futures 326bps

• Allowable Leverage = 3:1 $$ \left[\frac{A}{E} = \frac{3}{1}\right] \rightarrow \left{ \begin{array}{l} A = L + E \ 3 = 2 + 1 \ [100% = 67% + 33%] \end{array} \right. $$

Also: $R_F = 1.8%$ & Loan Rate = $[R_F + 0.35%] = [1.8% + 0.35%] = 2.15%$

& Cost of using Leverage to implement the TAA:

1. For every ₹100 invested in the ETF

   • ₹67 is financed via Debt
   • ∴ Leverage Cost = $(2.15% \text{ of } ₹67) = ₹1.44$
   • As a % of Total Invested = $(₹1.44/₹100) = 1.44%$ or $144$ bps.

2. For every ₹100 exposure via Futures: $F = S_0(1+R_F)^T$

   • Cost of Carry = $R_F = 1.8%$
   • $(1.8% \text{ of } ₹100) = ₹1.80$

TIP: Futures do not require any upfront investment.

• ∴ mgr can deposit the margin (equity) amount → $[33% \text{ of } ₹100] = ₹33$ and earn $R_F$.

• ∴ Interest Income = $(1.8% \text{ of } ₹33) = ₹0.594$

• ∴ Net Cost of Leverage:

  • Assets (Cost on ₹100) ₹1.80
  • • Equity (Inc on ₹33) ₹0.594
  • = Leverage (Net Cost) ₹1.206
  • As a % of Total Invested → $(₹1.206/₹100) = 1.21%$ or $121$ bps

Recommendation: Select Futures contract to execute the levered TAA (lowest total implementation cost at 295 bps).

#EXAMPLE

• Decision: Implement 1% overweight via futures (lower-cost alternative)
• Leverage structure: 3× (66.67% borrowed / 33.33% cash) → factor $0.6667$
• ETF borrowing rate: $1.80% + 35\text{ bps} = 2.15%$
• Futures funding: no investment-income offset at 3‑mo MRR ($1.80%$)
• Additional leverage cost
  • ETF: $\frac{5\text{M} \times 0.6667 \times 2.15%}{5\text{M}} = 1.43%$ (143 bps)
  • Futures: $\frac{5\text{M} \times 0.6667 \times 1.80%}{5\text{M}} = 1.20%$ (120 bps)
• Unlevered cost: ETF 27 bps · Futures 32 bps
• Total levered cost: ETF $27 + 143 = 170$ bps · Futures $32 + 120 = 152$ bps
• Advantage: Futures cheaper by 18 bps

Implementation Options: Advantages vs. Disadvantages