1. Definition of a risk measure (rm) and its applications in finance and insurance;
2. Classical examples of rm: Value-at-Risk (VaR), Expected Shortfall (ES), Expectiles;
3. Computing risk measures for discrete and continuous distributions;
4. Properties: Monotonicity, Translation Invariance, Positive homogeneity, subadditivity, convexity, comonotonicity, law-invariance; Convex and coherent rm;
5. Non subadditivity of VaR and its consequences on portfolio diversification;
6. Estimation of rm (Historical estimation, generalised quantile regression, maximum likelihood methods, bayesian approach);
7.Risk measurement for an aggregate position.
References:
1. Embrechts, P., F. Rudiger., and A. McNeil. "Quantitative risk management." Princeton Series in Finance, Princeton 10 (2005).
2. Föllmer, H., and A. Schied. Stochastic finance: an introduction in discrete time. Walter de Gruyter, 2011.
3. Jorion, P.. Value at risk: the new benchmark for managing financial risk. Vol. 3. New York: McGraw-Hill, 2007.