Spending Parameters
Spending Rule Formulas
Spending Analysis
Rule Comparison
Spending Path
Year-by-Year Projection
Formula Breakdown
Model Assumptions
- Investment returns are constant (expected return achieved every year)
- No stochastic modeling of returns, spending, or inflation
- Annual contributions and inflation rate are constant over the horizon
- Spending is withdrawn after investment returns are applied (end-of-year)
- Year 0 is a pure snapshot: seed spending is shown for reference but not deducted from the endowment
- Yale-Style Smoothing uses current-year post-return market value (not lagged); actual Yale endowment timing may differ
- Rolling average uses available years when fewer than the window size exist
- If endowment is depleted, actual spending is capped at available funds and simulation stops
- Real Growth uses the exact Fisher formula: (1 + nominal CAGR) / (1 + inflation) - 1
For educational purposes. Not financial advice. Market conventions simplified.
Understanding Endowment Spending Rules
What is an Endowment Spending Rule?
An endowment spending rule (or spending policy) determines how much an endowment distributes each year to fund operations, scholarships, research, or other purposes. The fundamental challenge is balancing two competing goals: providing adequate current funding while preserving the endowment's real (inflation-adjusted) purchasing power for future generations. This is known as the principle of intergenerational equity.
The Four Spending Rules
Simple Spending Rate
Formula: Spending = Rate × PostReturnMV
Applies a fixed percentage to current market value. Simple and transparent, but spending fluctuates directly with investment returns, creating budget volatility.
Rolling Average
Formula: Spending = Rate × Average(MV over N years)
Smooths spending by averaging market values over a window (typically 3-5 years). Reduces volatility but lags behind market movements.
Yale-Style Smoothing
Formula: S(t) = w × S(t-1) × (1+inf) + (1-w) × Rate × MV
Blends inflation-adjusted prior spending (weight w) with current endowment value (weight 1-w). Used by Yale, Stanford, and many large endowments. Produces very smooth, predictable distributions.
Cap-Floor Policy
Formula: Spending = Rate × MV, clamped to [Floor%, Cap%] of prior spending
Starts with the simple rule but constrains year-over-year changes. Prevents both excessive spending in bull markets and painful cuts in downturns.
Spending Volatility vs. Endowment Sustainability
There is a fundamental trade-off between spending smoothness and responsiveness to market conditions:
- More smoothing (Yale with high w, narrow cap-floor bands) provides budget predictability but can lead to over-spending in prolonged downturns or under-spending in prolonged bull markets
- Less smoothing (simple rule, wide cap-floor bands) keeps spending aligned with actual endowment performance but creates budget volatility that can disrupt operations
The spending volatility metric (standard deviation of year-over-year spending changes) quantifies this trade-off. Lower volatility means more predictable budgets.
If Real Return > Spending Rate, endowment grows in real terms
This is a necessary (but not sufficient) condition for long-term sustainability
Frequently Asked Questions
Disclaimer
This calculator is for educational purposes only and uses simplified, deterministic assumptions. Actual endowment management involves stochastic return modeling, spending policy committees, and consideration of donor restrictions, tax implications, and regulatory requirements. This tool should not be used for actual endowment spending decisions.
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Course by Ryan O'Connell, CFA, FRM
Portfolio Analytics & Risk Management Course
Master portfolio theory and risk management from fundamentals to advanced analytics. Covers modern portfolio theory, risk metrics, performance evaluation, and factor models.
- Endowment management and spending policy analysis
- Modern Portfolio Theory and efficient frontier construction
- Risk metrics: VaR, CVaR, drawdown analysis
- Hands-on exercises with real portfolio data