Plausible return and risk assumptions for portfolio simulations
Reference values for return and volatility in parametric Monte Carlo simulations, with a clear distinction between illustrative scenario assumptions and historically measured metrics.
Monte Carlo simulations require assumptions about returns and variability. The following values are illustrative scenario assumptions for model calculations. They are not return forecasts and do not constitute investment advice.
Scenario assumptions and historical measurements are not the same
Historically measured values refer to a specific index, observation period, currency and return definition. Changing the observation window can materially change the measured return or volatility.
Scenario assumptions are deliberately rounded test values for the parametric Monte Carlo model. They describe the scenario being simulated and are not expectations of future market returns.
All return assumptions in the examples below are nominal annual returns before fees and inflation. Volatility refers to the annualized standard deviation of nominal returns. Where applicable, mixed portfolios assume regular rebalancing.
Example presets for simulations
Money market / short-term deposits
Example allocation: 100% short-term deposits in the model currency
Assumed nominal return: 2.5% p.a.
Assumed volatility: 1% p.a.
Sensitivity range: return 1–4%; volatility 0–2%. Intended for nominally stable deposits; interest-rate and inflation risk should be assessed separately.
Bond-heavy portfolio
Example allocation: 20% global equities / 80% high-quality bonds
Assumed nominal return: 4.0% p.a.
Assumed volatility: 6% p.a.
Volatility sensitivity range: 4–10%. Duration, credit quality and currency hedging materially affect risk.
Defensive balanced portfolio
Example allocation: 40% global equities / 60% high-quality bonds
Assumed nominal return: 5.0% p.a.
Assumed volatility: 8% p.a.
Volatility sensitivity range: 6–12%. Equity/bond correlations can change, particularly during inflationary crises.
Balanced portfolio
Example allocation: 60% global equities / 40% high-quality bonds
Assumed nominal return: 6.0% p.a.
Assumed volatility: 11% p.a.
Volatility sensitivity range: 9–15%. A useful reference case for accumulation and withdrawal simulations.
Equity-heavy balanced portfolio
Example allocation: 80% global equities / 20% high-quality bonds
Assumed nominal return: 6.5% p.a.
Assumed volatility: 14% p.a.
Volatility sensitivity range: 11–18%. Larger drawdowns than in a 60/40 portfolio are possible.
Global equities
Example allocation: 100% developed-market equities, broadly diversified
Assumed nominal return: 7.0% p.a.
Assumed volatility: 16% p.a.
Volatility sensitivity range: 14–20%. Approximation for a broadly diversified equity portfolio, not a guaranteed return.
US equities
Example allocation: 100% broadly diversified US equity market
Assumed nominal return: 7.0% p.a.
Assumed volatility: 17% p.a.
Volatility sensitivity range: 15–22%. US-specific concentration and valuation risks should be considered.
Emerging-market equities
Example allocation: 100% emerging-market equities
Assumed nominal return: 7.0% p.a.
Assumed volatility: 20% p.a.
Volatility sensitivity range: 17–25%. Higher country, currency and political risks.
Higher-risk equities
Example allocation: 100% concentrated equity portfolio
Assumed nominal return: 7.0% p.a.
Assumed volatility: 25% p.a.
Volatility sensitivity range: 20–35%. Generic stress scenario; it should not be used as a benchmark.
Important: These values are illustrative model presets. They are not future return forecasts derived from the sources listed below. Inputs should be adapted to the portfolio actually being analyzed, and sensitivity analyses should be performed.
Historical anchors for context
MSCI World
For the MSCI World in USD, an annualized volatility of 14.86% was reported for the ten years through 31 August 2026. Other observation windows produce different values. A historical measurement is not a long-term forecast.
An MSCI factsheet with data through 30 April 2026 reports 15.91% for a different ten-year window. This illustrates how strongly metrics can depend on the valuation date, observation window and methodology used.
Historical monthly return series
The Kenneth R. French Data Library provides historical monthly return series for markets including the US market, developed markets and emerging markets. Return and volatility should be calculated separately from the exact dataset used by the simulation.
Why allocation weights alone are not enough for mixed portfolios
Portfolio volatility depends not only on the volatility of each asset class but also on the correlation between them.
sigma_portfolio = sqrt(wA² × sigmaA² + wB² × sigmaB² + 2 × wA × wB × sigmaA × sigmaB × rhoAB)
Example: with 60% equities at 16% volatility and 40% bonds at 6% volatility, portfolio volatility is approximately 9.9% when correlation is 0, approximately 11.0% when correlation is +0.5, and approximately 9.2% when correlation is −0.3. The 11% value used in the 60/40 preset is therefore a model assumption, not an immutable property of a 60/40 portfolio.
Use in the RedneckWorks simulator
Selecting a portfolio preset should populate return and volatility only as starting values. The input fields remain editable.
The UI should clearly state: “Illustrative assumptions, not a forecast. Please adjust them to your own portfolio.”
Scenario assumptions and historically measured values should be treated as distinct data types.
For historical bootstrap simulations, the actual monthly returns of the selected dataset should be used instead of scenario presets.
Currency, nominal versus real values, fees, inflation, dividends, observation period, data date and rebalancing assumptions should be disclosed consistently.
Comparison runs can use the same seed and path count so that only one parameter changes at a time. Using the same seed does not make the underlying assumptions more realistic.
Methodological validation
Check historical monthly return series for missing observations, outliers, currency, dividend treatment, inflation treatment and usage rights.
Calculate historical annualized volatility transparently as std(monthly returns, ddof=1) × sqrt(12), and state the observation window.
Report geometric annualized return separately from the arithmetic mean return.
Construct mixed portfolios from synchronized historical monthly series, incorporating correlations and an explicitly defined rebalancing policy.
For withdrawal simulations, test sequence-of-returns risk, extreme losses and different inflation regimes.
Test the inverse solver for stability with a high path count and a documented seed. An exactly reproducible model success rate is not evidence of the same real-world probability.
Sources
MSCI World Index Factsheet – April 2026
Kenneth R. French Data Library
Vanguard – Model Portfolio Allocation
Vanguard – Global 60/40 Portfolio
Compilation date: 27 September 2026. Before using these values as production defaults, verify the current data and methodology again.