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FRM Part I · FRM Exam Part I · Simulation and Bootstrapping

A firm uses Monte Carlo simulation to value a portfolio, drawing correlated asset returns from a multivariate normal model calibrated in a calm market. Which is the most important model-risk limitation of this approach?

Monte Carlo results depend entirely on the assumed distribution and calibrated parameters. A multivariate normal model fitted in calm markets will miss fat tails and the rise in correlations during stress, so risk can be understated despite many simulated trials.

  1. ASimulation cannot handle more than two assets at once
  2. BResults are only as good as the assumed distribution and parameters, so fat tails and correlation breakdown in stress will be missedCorrect
  3. CThe simulated results are always identical across runs, hiding uncertainty
  4. DSimulation requires historical data on every possible future scenario

Explanation

Monte Carlo output depends on the assumed model and calibration. A normal model calibrated in calm conditions understates tail risk and stress-period correlations. Simulation handles many assets, results vary with random seed, and it does not require historical data for every scenario.

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