by Alexander Lipton, Christoph Reisinger, Vadim Kaushansky
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Suggested foundations for optimisation and numerical methods and risk measurement and stress. Specialist prerequisites vary by paper.
Read the linear algebra used to define a numerical problem.
Identify the objective, constraints and conditions for an optimum.
Represent losses as a distribution rather than one forecast.
Distinguish a loss quantile from the average loss in the tail.
Understand identifiability and fitted model parameters.
Separate model error from unstable numerical computation.
Compare risk estimators and their assumptions.
Connect diversification, common risk factors, investor views, and the properties of a risk measure.