Monte Carlo Simulation for Retirement
Monte Carlo simulation is a stress test for your retirement plan. Instead of one smooth, average forecast, it runs your portfolio through thousands of different market histories — some kind, some brutal — and shows how often your money would have lasted through each. The result is a probability of success and a realistic range of possible endings.
What is a Monte Carlo simulation?
Named after the famous casino in Monaco, the method leans on randomness to explore what could happen rather than assuming one tidy path. For retirement, a Monte Carlo simulation starts with the details of your plan — how much you have saved, what you spend each year, how long retirement needs to last, and the returns and volatility you expect from your portfolio. It then generates thousands of independent scenarios, or "runs", of that plan. Every run is a different, randomly assembled sequence of yearly returns — some years up, others down — applied to your balance while your withdrawals are taken out along the way. When all the runs finish, you get a distribution of outcomes: the share of runs that survived, plus the range of ending balances from the luckiest to the unluckiest.
Why a single average return isn't enough
Planning with one flat number — say "I'll earn 8% a year" — hides the two forces that actually decide how retirement feels: volatility and the order of returns. When a market dip arrives matters just as much as the dip itself. If the down years land while you're selling units to fund living costs, the same 8% average that looked fine on a spreadsheet can leave you short. That is the essence ofsequence-of-returns risk. Monte Carlo simulation puts the variability back in, letting each simulated year wobble around the average the way real markets do.
How the simulation works
The simulator follows five steps. First you set your assumptions: starting corpus, annual spending, investment horizon, the expected (average) annual return, and its volatility. Second, the simulator draws a random return for year one — centred on your expected return but scattered by the volatility figure. Third, it applies that return to your balance, subtracts that year's withdrawals, and moves to year two, repeating until retirement ends. Fourth, it repeats the whole exercise hundreds or thousands of times, each run receiving a freshly randomised sequence of returns. Fifth, it tallies the results: how many runs finished with money left, and what the balances looked like at different percentiles. Runs that fail usually run out late in retirement, which is why long horizons can be surprisingly sensitive to small changes in assumptions.
What "success probability" means
The headline number is simple: the percentage of simulated runs in which your portfolio never ran dry. If the simulator reports 85%, it means 85 out of 100 generated histories kept your money intact under your stated assumptions — not that real life hands you an 85% chance. Because every run is built from the assumptions you choose, the figure is conditional: change the expected return or your spending and the number moves. Treat it as a robustness check and a conversation starter, not a verdict.
How the tool on this site works
The Monte Carlo simulator on this site applies exactly this method to your plan. You enter your portfolio size, yearly withdrawals and assumptions; the tool simulates between 500 and 2,500 return paths and reports the proportion of runs that survive, along with how the balance evolves across percentile outcomes. It's the same engine you can use to compare a fixed withdrawal plan against abucket strategy, or to sanity-check the target from theFIRE retirement calculator.
What Monte Carlo results are NOT
They are not guarantees, and they are not predictions about what the market will do. They are arithmetic play-throughs of the assumptions you supply, so the output is only as sensible as the inputs. Assume a heroic return and nearly every run succeeds; assume sky-high volatility and many fail. Real life also includes fees, taxes, inflation surprises and human decisions the model cannot foresee, and financial returns aren't perfectly "normal" in the statistical sense. Use Monte Carlo as one input among several in your planning — alongside diversification, common sense and a margin of safety.
Monte Carlo retirement planning in India
The method is currency-agnostic. A portfolio measured in ₹, spending in ₹ and an Indian mix of equity and debt funds work exactly the same way in the simulator as in any other market — the mathematics don't care about the symbol on the money. What changes from market to market is the reasonableness of the assumptions you feed in: expected returns, inflation and volatility have behaved differently in India than in the US, so importing overseas assumptions blindly can mislead you. Keep your inputs realistic and the results will be more honest.
Educational resource, not financial advice. Monte Carlo results are estimates produced from your own assumptions; real returns are uncertain and past performance is not a guide to the future. For personal financial decisions, consider consulting a qualified professional.
Frequently Asked Questions
What is Monte Carlo simulation?
Monte Carlo simulation is a method that tests a plan against many thousands of randomly generated possibilities instead of assuming a single smooth path. For retirement, it applies thousands of different return sequences to your portfolio, withdrawing your spending each year, and reports how often the money lasted and the range of ending balances.
How does Monte Carlo simulation work for retirement?
You set your assumptions — starting corpus, annual spending, time horizon, expected return and volatility. The simulator then generates hundreds or thousands of separate "runs", each a different random sequence of yearly returns. Every run applies those returns and your withdrawals year by year, and at the end the tool counts how many runs ended with money remaining.
What is a good success probability?
There is no universal answer; it depends on your risk tolerance and how much flexibility you have. Many planners treat 80–90% as a reasonable target range, but the number only makes sense relative to your assumptions. A higher target typically means a lower withdrawal rate, more savings, or more years of work — a trade-off only you can judge.
How many simulations are enough?
More runs give smoother, more stable percentages, but beyond a few thousand the answer barely changes. Somewhere between 1,000 and 10,000 runs is plenty for practical planning. The tool on this site offers 50 to 2,500 simulations — use the lower counts for a quick estimate and the higher ones for stable percentages.
Are Monte Carlo results a guarantee?
No. Results are estimates produced from the assumptions you supply — they are a stress test of the plan you describe, not a prediction of what markets will do. Real life includes fees, taxes, changing spending and events the model can never fully capture, so treat the output as one input to your decisions, not a promise.
What inputs do I need?
You need your current retirement corpus, how much you plan to withdraw each year, how long retirement must last, and reasonable assumptions for your portfolio's average return and volatility. Better inputs give more honest results, so base them on your actual portfolio and a defensible view of your market rather than optimistic hopes.