by George Samartzis, Nikitas Pittis
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Suggested foundations for monte carlo and numerical simulation and portfolio construction. Specialist prerequisites vary by paper.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Understand the covariance structure of portfolio risk.
Read objectives, constraints and optimality conditions.
Turn a discounted payoff into an estimator.
Study dependence and alternatives to independent sampling.
Construct allocations and examine sensitivity to estimated inputs.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.
Connect diversification, common risk factors, investor views, and the properties of a risk measure.