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