Use of stochastic and mathematical programming in?portfolio theory and practice

Standard finance portfolio theory draws graphs and writes equations usually with no constraints and frequently in the univariate case. However, in reality, there are multivariate random variables and multivariate asset weights to determine with constraints. Also there are the effects of transaction costs on asset prices in the theory and calculation of optimal portfolios in the static and dynamic cases. There we use various stochastic programming, linear complementary, quadratic programming and nonlinear programming problems. This paper begins with the simplest problems and builds the theory to the more complex cases and then applies it to real financial asset allocation problems, hedge funds and professional racetrack betting. This paper is based on a keynote lecture at the APMOD conference in Madrid in June 2006. It was also presented at the London Business School. Many thanks are due to APMOD organizers Antonio Alonso-Ayuso, Laureano Escudero, and Andres Ramos for inviting me and for excellent hospitality in Madrid. Thanks are also due to my teachers at Berkeley who got me on the right track on stochastic and mathematical programming, especially Olvi Mangasarian, Roger Wets and Willard Zangwill, and my colleagues and co-authors on portfolio theory in finance and horseracing, especially Chanaka Edirishinge, Donald Hausch, Jarl Kallberg, Victor Lo, Leonard MacLean, Raymond Vickson and Yonggan Zhao.