Definition
Monte Carlo Simulation
Monte Carlo simulation is a technique that runs thousands of randomised scenarios to model the range of possible outcomes for a strategy or portfolio, revealing the distribution of returns, drawdowns and risk.
Indian quants use Monte Carlo methods to stress-test strategies by resampling or shuffling historical returns, simulating many alternative paths the market could have taken. This shows how lucky or unlucky a single backtest result might be and estimates the worst-case drawdown an investor should expect.
Monte Carlo is also used in financial planning to project goal achievement under uncertain returns, and in derivatives to price path-dependent options. Its quality depends on the assumptions fed in; garbage assumptions produce confidently wrong distributions, so the inputs deserve as much scrutiny as the output.
Related terms
- BacktestingBacktesting is the process of simulating a trading strategy on historical data to estimate how it would have performed, including returns, drawdowns and risk, before committing real capital.
- Risk ParityRisk parity is a portfolio construction approach that allocates capital so that each asset or asset class contributes equally to total portfolio risk, rather than weighting by capital invested.
- Kelly CriterionThe Kelly criterion is a position-sizing formula that determines the fraction of capital to risk on a bet or trade to maximise the long-run growth rate of wealth, given the edge and odds.
- Maximum DrawdownMaximum drawdown is the largest peak-to-trough decline in the value of a portfolio or strategy over a period, measuring the worst loss an investor would have suffered before a new high was reached.
Plain-English explainer from The Dispatch Investors Encyclopedia. General information, not financial advice.