Book: Risk and Asset Allocation

Chapters

Ch 1/2: Uni- and multi-variate statistics, see here
Ch 3: Quest for Invariance, see here
Ch 3: Projection of invariants to investment horizon, see here
Ch 3: Pricing of individual securities, see here
Ch 3: Linear factor models (PCA, time series,…), see here
Ch 3: Swaps modeling using Principal Component Analysis, see here
Ch 4: Multivariate estimation (non-parametric, MLE, shrinkage, robust,…), see here
Ch 5: Risk evaluation (stochastic dominance, expected utility, VaR, CVaR, spectral measures,…), see here
Ch 6: Portfolio optimization (mean-variance, cone programming, benchmark allocation,…), see here
Ch 7: Bayesian estimation, see here
Ch 8: Estimation risk evaluation, see here
Ch 9: Estimation risk and allocation optimization (Bayes, Black-Litterman, robust,…), see here
App A: Linear Algebra, see here
App B: Functional Analysis, see here


Technical proofs and exercises

Tedious proofs and technical results, challenges, pitfalls and step-by-step solutions with MATLAB code, see ARPM Lab Exercises.


Applications

Python and MATLAB code for advanced risk and portfolio management, see here.

Employers

Hundreds of financial institutions worldwide have trusted ARPM with the education and the growth of their talent pool.