Worked example
Example with the default numbers — change them above.
Corpus on the day you retire: ₹3,00,00,000 · Monthly spending in the first year: ₹75,000 · Retirement age: 45 · Money should last until age: 90 · Equity share: 60% · Debt share: 30% · Gold share: 10% · Show amounts adjusted for inflation: Yes
In 86% of 5,000 simulated futures, spending ₹75,000 a month from ₹3 Cr, your money lasts to age 90. Spending about ₹69,375 a month would raise that to 90%.
| Chance your money lasts to 90 | 86% · Borderline: 704 of 5,000 futures ran out |
|---|---|
| Safe monthly spending at 90% odds | ₹69,375 · 2.77% of the corpus in year one; 90.2% of futures last |
| Your withdrawal rate | 3% · ₹9,00,000 in year one, then rising with inflation |
| Typical corpus left at 90 | ₹3.74 Cr · Median future, in retirement-year money |
| Worst 1 in 10 futures at 90 | ₹0 · Money had run out |
How the FIRE Monte Carlo Simulator works
- For each simulated future, the simulator draws a return for equity, debt and gold and an inflation rate for every year, using the averages and swings you set. How closely equity, debt and gold move together is fixed in the simulator, not set by you.
- Your corpus starts on the day you retire, split across the three assets in your chosen mix.
- Each month your spending is withdrawn first, then the rest grows by that year's portfolio return spread evenly over 12 months.
- Each year your spending rises by that future's inflation, and the portfolio is rebalanced to your mix unless you choose not to.
- A future succeeds if every withdrawal up to your plan-to age is paid in full; the success rate is the share of futures that succeed.
- The safe-spending figure is found by testing higher and lower spending on the same futures until about 90% of them succeed.
One simulated year
Asset return R = exp(μ + σ × Z) − 1, with Z correlated across equity, debt and gold
σ² = ln(1 + s² ÷ (1 + m)²), μ = ln(1 + m) − σ² ÷ 2
Portfolio return P = Σ weight × R (after yearly rebalancing)
Each month: Balance = (Balance − spending) × (1 + P)^(1/12)
m = expected return, s = volatility you enter for that asset
Next year's spending = spending × (1 + that year's inflation)Assumptions and limits
- Returns follow a lognormal model with the averages and volatilities you enter; they are round planning figures, not historical statistics or forecasts. The average is treated as an arithmetic mean: at 12% with 18% volatility the typical compounded equity return is about 10.6%.
- Extreme years are rarer than in real markets: under the default equity assumptions a one-year fall of 40% or more has roughly a 1-in-15,000 chance, yet the Nifty 50 fell about 50% in 2008. Treat low failure rates with caution.
- How closely equity, debt and gold move together is fixed (equity–debt 0.1, equity–gold −0.1, debt–gold 0.1) and cannot be edited.
- Each year is independent of the last: the model has no booms followed by busts, no mean reversion and no regime changes.
- Inflation is drawn each year from a normal distribution with no memory, independent of returns, with a floor at −50%; debt returns do not rise when inflation does, so long high-inflation runs such as 2010–13 are under-represented.
- Spending always rises with inflation and never adapts to markets; real retirees usually cut back in bad years, which improves their odds.
- Tax, fund costs beyond the returns you enter, and other income such as pensions or rent are not modelled.
- The random seed is fixed, so the same inputs give the same result; results still vary slightly with the number of futures.
Frequently asked questions
What is a FIRE Monte Carlo simulator?
It is a tool that tests a retirement plan against thousands of possible futures instead of one average return. Each future has its own sequence of good and bad years for equity, debt, gold and inflation. Counting how many futures pay every withdrawal to your chosen age gives a probability of success, a more honest picture than a single projection for an early retirement in India.
What success rate should I aim for?
Planners commonly look for roughly 85% to 95%. This page labels 90% and above as Comfortable, 75% to 90% as Borderline and below 75% as Risky, but these are rules of thumb. Aiming for 100% usually means working years longer than needed. A lower rate can be fine if you can cut spending, earn some income or have a pension to fall back on.
Why use Monte Carlo instead of a fixed return?
Because the order of returns matters once you are withdrawing. Two retirees with the same average return can end very differently if one meets a crash in year two and the other in year twenty. This is called sequence-of-returns risk. A fixed-return calculator cannot show it; Monte Carlo shows how often bad sequences sink a plan and when.
How do I read the fan chart?
Each line is a percentile of the corpus across all simulated futures at each age. The middle line is the median: half the futures are above it. The lowest line is the 10th percentile, so one future in ten does worse; the highest is the 90th. A widening fan means more uncertainty. If the lowest lines hit zero before your plan-to age, those futures failed.
Where do the default returns and volatilities come from?
They are round, long-run planning figures for Indian markets: 12% for equity with 18% volatility, 7% for debt with 4%, 8% for gold with 15%, and 6% inflation with 1.5%. They are not forecasts or verified historical statistics. Change them under Advanced options to match your own view, and test cautious figures as well as hopeful ones.
How should I use the safe spending figure?
Treat it as the first-year spending at which about 90% of the simulated futures last to your chosen age, under your assumptions. It is a planning anchor, not a promise. Many retirees start near such a figure and review every year, cutting back a little after bad markets and allowing small raises after good ones, which improves real-world odds.
Why do I get the same result every time?
The simulator uses a fixed random seed, so the same inputs always produce the same set of futures and the same answer. That makes results reproducible and easy to share as a link. If you change the number of futures, the percentages can shift by a point or two, which is a useful reminder of how precise these estimates really are.
What does this simulator not capture?
It uses a simple statistical model, so it misses long slumps, crashes more extreme than the model allows, and patterns such as markets recovering after falls. It ignores tax, other income such as pensions or rent, and flexible spending. Big one-off costs, such as a medical emergency or supporting family, are not included either. Treat the result as one useful input.
Sources
- Ministry of Statistics and Programme Implementation: consumer price index (inflation) data
- Reserve Bank of India: inflation, interest-rate and household-finance statistics
- AMFI: mutual fund data and investor education (SIP and SWP basics)
- SEBI: investor education on market risk
Logic and rates last verified on .
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Guides that go deeper
See worked examples
The corpus and monthly SIP to retire at 40, 45, 50 or 55 on ₹20,000 to ₹2 lakh a month.
- How much corpus do I need to retire at 40 with ₹20,000 monthly expenses? — Corpus ₹1.23 Cr, SIP ₹44,500 a month
- How much corpus do I need to retire at 40 with ₹30,000 monthly expenses? — Corpus ₹1.84 Cr, SIP ₹66,500 a month
- How much corpus do I need to retire at 40 with ₹50,000 monthly expenses? — Corpus ₹3.07 Cr, SIP ₹1,11,000 a month