Monte Carlo simulation
A computational technique that uses random sampling to model probability distributions of outcomes, used in finance to assess risk and value complex instruments.
Example
“The Monte Carlo simulation ran 10,000 scenarios showing a 90% probability the portfolio would last 30 years.”
Memory Tip
MONTE CARLO = run thousands of random scenarios to see the full range of possible outcomes.
Why It Matters
Understanding Monte Carlo simulation helps you make better financial decisions by showing you the range of possible outcomes rather than relying on single predictions. This is crucial for retirement planning, investment allocation, and understanding whether you have enough savings to reach your goals under various market conditions.
Common Misconception
Many people think Monte Carlo simulation predicts the future or tells you what will happen. In reality, it shows you the probability of different outcomes occurring, helping you understand risk and prepare for multiple scenarios rather than forecasting any specific result.
In Practice
A financial advisor might use Monte Carlo simulation to analyze a retirement plan by running 10,000 scenarios of market returns, inflation rates, and spending patterns over 30 years. The simulation might show that in 9,200 scenarios the client does not run out of money, meaning there is a 92 percent success rate for their retirement plan with their current savings and withdrawal strategy.
Etymology
Named after the Monte Carlo casino in Monaco — using RANDOM NUMBERS like casino games to simulate many possible outcomes.
Common Misspellings
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Related Terms
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See Also
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