Lesson 4 of 6 · All lessons in this module
  1. The lognormal distribution
  2. From continuous returns to lognormal pricesVideo lesson
  3. Estimating and annualising volatility
  4. Monte Carlo simulation: what it is and what it is for
  5. Running a Monte Carlo simulation, step by step
  6. Bootstrap resampling

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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