by Andrew W. Lo, Craig A. MacKinlay
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Suggested foundations for monte carlo and numerical simulation and probability and statistical reasoning. Specialist prerequisites vary by paper.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Read the distributions and expectations used in probabilistic arguments.
Interpret expectations after information is observed.
Turn a discounted payoff into an estimator.
Study dependence and alternatives to independent sampling.
Assess uncertainty rather than treating an estimate as a fact.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.
Move from conditioning and expected values to timed problems with a defensible solution.