Monte Carlo

Monte Carlo Portfolio Simulation for Outcome Ranges

Stress-test plans with simulated return paths, percentile bands, and failure-rate context instead of relying on a single deterministic line.

Monte Carlo is for plan-risk questions: sequence risk, success ranges, and distribution-aware planning outcomes.

This page covers what the simulation models, what inputs matter most, and how to interpret the percentile output.

What this workflow adds

  • Percentile ranges instead of a single deterministic outcome.
  • Stress testing for contribution and withdrawal plans.
  • Context for sequence-of-returns risk.

When not to use it

Monte Carlo is not a substitute for explicit historical backtests when the question is about how a specific strategy behaved. Use it when you need distributions, not when you need exact historical path replay.

Inputs that matter most

  • Return and volatility assumptions.
  • Cash-flow schedule and horizon length.
  • How many paths are enough for the decision you are making.

Best companion reads

  • Portfolio Backtest for historical context.
  • Verification Suite for engine coverage.
  • Pricing if you need larger simulation capacity.

FAQ

Does Monte Carlo tell me what will happen?

No. It gives a probability distribution under stated assumptions. Review the return, volatility, and cash-flow inputs carefully, since the output is only as reliable as those choices.

Related Pages