by Adel Cherchali, Alexandre Boumezoued, Gilles Pagès, Mathieu Truc, Vincent Lemaire
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Suggested foundations for monte carlo and numerical simulation and risk measurement and stress. Specialist prerequisites vary by paper.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Represent losses as a distribution rather than one forecast.
Distinguish a loss quantile from the average loss in the tail.
Turn a discounted payoff into an estimator.
Study dependence and alternatives to independent sampling.
Compare risk estimators and their assumptions.
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.