by Jean-Philippe Bouchaud, Laurent Laloux, Marc Potters, Pierre Cizeau
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Suggested foundations for probability and statistical reasoning and risk measurement and stress. Specialist prerequisites vary by paper.
Represent losses as a distribution rather than one forecast.
Interpret expectations after information is observed.
Distinguish a loss quantile from the average loss in the tail.
Assess uncertainty rather than treating an estimate as a fact.
Compare risk estimators and their assumptions.
Check forecasts against realised losses.
Move from conditioning and expected values to timed problems with a defensible solution.
Connect diversification, common risk factors, investor views, and the properties of a risk measure.