This paper is on the portfolio optimization problem for which two generic models are presented in the context of a proprietary solver called GENO: the first is a pseudo-dynamic model meant for the single holding-period case; the second is a truly dynamic model that applies to both the single and the multi-period scenario. Both models can handle practical restrictions such as exposure, cardinality and round-lot constraints; both can accommodate non-traditional risk measures; and the usual two-element criterion set comprising ‘portfolio reward’ and ‘portfolio risk’ may be augmented by any number of sub-objectives deemed necessary. The paper also presents a ‘robust optimization’ model of the portfolio problem that explicitly accounts for data uncertainty; the robust model can also accommodate sophisticated risk measures, and one such function is presented in Appendix B. The compromise solution concept is used to compute portfolios that are Pareto-efficient; several numerical examples show that the portfolios thus found are not only optimal in the compromise sense, but they also have a competitive (and often the highest) reward-risk ratio—a proxy measure of the Sharpe ratio. A limited empirical analysis shows that the robust model is superior to its non-robust counterpart in terms of both nominal performance, and the potential for avoiding “opportunity costs” due data uncertainty.
Siwale, I. (2013): Practical Portfolio Optimization. Technical Report No. RD-20-2013, London: Apex Research Ltd.