by Azar Louzi, Jonathan Spence, Noufel Frikha, Stéphane Crépey
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Suggested foundations for risk measurement and stress and monte carlo and numerical simulation. Specialist prerequisites vary by paper.
Represent losses as a distribution rather than one forecast.
Distinguish a loss quantile from the average loss in the tail.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Compare risk estimators and their assumptions.
Check forecasts against realised losses.
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.