Monte carlo simulation
Drawing many random scenarios from assumed distributions for a model's uncertain inputs and studying the spread of outcomes. Precise about the consequences of the assumptions, silent on whether they are right.
Where you will meet it
Monte carlo simulation appears in 7 learning outcomes on the CFA syllabus. A few of them:
- Explain Monte Carlo simulation and what it is used for
“Monte Carlo simulation generates many random draws from assumed distributions for the uncertain inputs of a model, computes the outcome for each, and uses the resulting distribution of outcomes to answer questions a single point estimate cannot.”
- Explain how a Monte Carlo simulation of forward rates works and where it is applied
“Monte Carlo forward-rate simulation generates many random paths of future rates, often monthly, using an assumed volatility and a model of rate behaviour.”
- Contrast three ways to estimate VaR: Monte Carlo simulation, historical simulation, and the parametric (variance–covariance) method
“Monte Carlo simulation draws many scenarios from an assumed model, fully revalues the portfolio in each, and reads off the percentile; it suits complex instruments and can include fat tails, but depends on the model and is computationally heavy.”
- Explain how scenario analysis and Monte Carlo simulation are used to stress-test an asset allocation's robustness
“Monte Carlo simulation generates thousands of return paths from assumed distributions, applies the cash flows and rules to each, and reports the distribution of terminal wealth and the probability of meeting spending or funding goals.”
Learn it properly
Practise it
Practise monte carlo simulation until it is automatic: questions that explain every wrong option.
Take the free diagnostic →