by Engel John C. Dela Vega, Len Patrick Dominic M. Garces, Tim J. Boonen
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Suggested foundations for risk measurement and stress and stochastic processes and pricing. 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 what it means to condition on current information.
Follow Brownian dynamics and the second-order term in Itô calculus.
Compare risk estimators and their assumptions.
Check forecasts against realised losses.
Connect stochastic dynamics to no-arbitrage prices.
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.