This book discusses modern portfolio optimization and applications. It is intended for quantitative finance professionals and graduate students in finance, operation research and applied mathematics. The software S+NuOPT is used for the optimization method and the software S-Plus Robust Library and S+Bayes Library are used for the modern statistical methods. The data set is from CRSP.
The first chapter reviews the Markowitz portfolio optimization with constraints and a number of variations are briefly discussed. Some special NuOPT functions are used for the optimization. The second chapter extends to general optimization problems such as non-quadratic utility and multi-stage stochastic optimization. A powerful modeling language SIMPLE in NuOPT is used.
The third chapter includes more advanced issues such as optimization with multiple benchmarks, risk regimes and handling transaction cost. Chapter four discusses the resampling techniques which understand the impact of estimation error in the input of means and covariances on the output of portfolio weights. It discusses two resampling methods: parametric bootstrap and nonparametric bootstrap. Chapter five discusses the scenario optimization with non-normality of returns. It also compares CVaR with VaR. Chapter six focuses on more statistical methods in portfolio construction. It deals with the robust statistical model-fitting method which is not influenced by outliers and the S-Plus Robust library is used. Chapter seven discusses modern Bayes modeling which allows the manager to combine prior information to arrive at a decision.