Monte Carlo
Monte Carlo generates many possible future paths from one return source. It is used to inspect outcome ranges, failure rates, and path sensitivity rather than a single historical track.
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Open Monte Carlo →When To Use It
- Estimate a range of ending wealth over a fixed horizon.
- Test a withdrawal policy against many simulated paths.
- Compare how inflation, spending, or return-model choices change tail outcomes.
For the exact historical behavior of a specific portfolio configuration, use Portfolio Backtest instead. See Historical Backtest vs Monte Carlo for a comparison.
Return Sources
| Source | Model | What It Uses |
|---|---|---|
| Parametric assumptions | Parametric (Log-Normal) | Annual mean return and annual volatility entered in the form. |
| Historical returns | Historical Bootstrap | Resampled returns from a prior backtest or Library portfolio. |
| Historical blocks | Block Bootstrap | Contiguous blocks of historical returns, which preserve short serial patterns. |
Main Inputs
- Initial value: starting portfolio value in dollars.
- Years: simulation horizon.
- Simulations: number of generated paths.
- Random seed: optional seed for reproducible results.
- Inflation model: whether nominal cashflows stay fixed or move with inflation assumptions.
- Withdrawal strategy: the spending rule used during the run.
Withdrawal Strategies
Each strategy determines how much is withdrawn from the portfolio each period. Fixed strategies keep spending constant; adaptive strategies adjust based on portfolio performance, market conditions, or remaining time horizon.
- Fixed Dollar: withdraw a constant inflation-adjusted dollar amount each year. Spending is predictable, but the portfolio bears all market risk.
- Percent of Portfolio: withdraw a fixed percentage of the current portfolio value each year. Spending fluctuates with performance, but the portfolio never reaches zero.
- Guardrails: start from a target withdrawal rate and inflate each year, but clamp spending when the implied rate drifts outside a floor/ceiling band. Balances income stability with portfolio protection.
- Variable Percentage (VPW): withdraw a percentage that changes each year based on remaining time horizon and equity allocation. Uses an amortization formula (PMT-based) to spread the portfolio over the remaining years, drawing more as the horizon shortens.
- Guyton-Klinger: start from an initial withdrawal rate and apply two path-dependent rules each year. The capital preservation rule reduces spending when the implied rate exceeds a threshold; the prosperity rule increases it when the rate falls below a threshold. Cuts and raises are percentage adjustments (typically 10%).
- CAPE-Based: set annual spending as a fraction of the portfolio scaled by the inverse of the CAPE (Shiller PE) ratio, clamped to a floor and ceiling rate. Withdraws less when valuations are high and more when they are low.
- RMD-Based: divide the portfolio by the IRS Uniform Lifetime Table divisor for the current age each year. Spending rises naturally as the divisor shrinks with age. For ages below 72, extrapolated divisors produce conservative (smaller) withdrawals.
Read The Outputs
- Fan chart: percentile bands of portfolio value through time.
- Terminal wealth: the distribution of ending values across paths.
- Success rate: share of paths that complete the horizon without reaching zero.
- Tail metrics: measures focused on adverse outcomes rather than the median path.
- Safe withdrawal rate: estimated spending level that satisfies the configured confidence target when that analysis is enabled.
Median and average outcomes are only part of the result. The lower percentiles and failure metrics describe downside behavior.
Library Portfolios And Ticker Modifiers
When the simulation uses a Library portfolio, its historical return series becomes the input to the Monte Carlo model. If that portfolio includes ticker modifiers, the transformed return series is used automatically.