Lesson 4 of 6 · 12 min
Monte Carlo simulation: what it is and what it is for
Monte Carlo simulation draws a very large number of random scenarios from distributions you specify, then reads the answer off the resulting distribution of outcomes.
In short
- Monte Carlo simulation generates a very large number of random samples from specified probability distributions to show the likelihood of a range of results.
- Use 1, risk and return: simulate a portfolio's profit and loss over a horizon and derive performance and risk measures from the simulated distribution.
- Use 2, valuing complex securities with no analytic pricing formula, such as path-dependent contingent claims and mortgage-backed securities with embedded options.
- Use 3, sensitivity ("what if") analysis: because you control the assumptions, you can change them and watch the output respond.
- Limits: results are statistical estimates, not exact values, and they give less insight into cause and effect than an analytical formula. Simulation complements analytical methods.
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