In real investment situations, one desires to only minimize downside risk or portfolio loss without affecting the upside potentials. This can be accomplished by mean semi-variance optimization but not by mean variance. In the Black-Scholes setting, this paper proposes for the very practical yet intractable dynamic mean semi-variance portfolio optimization problem, an almost analytical solution. It proceeds by reducing the multi-dimensional portfolio selection problem to a one-dimensional optimization problem, which is then expressed in terms of the normal density, leading to a very simple and efficient numerical algorithm. A numerical comparison of the efficient frontier for the mean variance and semi-variance portfolio optimization problem is presented.
|Number of pages||10|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 1 Dec 2003|
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)