Post by Sapphire Capital on Aug 26, 2008 21:00:29 GMT 4
Distributed Optimisation of a Portfolio's Omega
Manfred Gilli
University of Geneva; Swiss Finance Institute
Enrico Schumann
University of Geneva
July, 07 2008
Swiss Finance Institute Research Paper No. 08 - 17
Abstract:
We investigate portfolio selection with alternative objective functions in a distributed computing environment. In particular, we optimise a portfolio's 'Omega' which is the ratio of two partial moments of the returns distributions. Since finding optimal portfolios under such performance measures and realistic constraints leads to non-convex problems, we suggest to solve the problem with a heuristic method called Threshold Accepting (TA). TA is a very flexible technique as it requires no simplifications of the problem and allows for a straightforward implementation of all kinds of constraints. Applying this algorithm to actual data, we find that TA is well-adapted to optimisation problems of this type. Furthermore, we show that the computations can easily be distributed which leads to considerable speedups.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1156355_code623849.pdf?abstractid=1156355&mirid=1
Manfred Gilli
University of Geneva; Swiss Finance Institute
Enrico Schumann
University of Geneva
July, 07 2008
Swiss Finance Institute Research Paper No. 08 - 17
Abstract:
We investigate portfolio selection with alternative objective functions in a distributed computing environment. In particular, we optimise a portfolio's 'Omega' which is the ratio of two partial moments of the returns distributions. Since finding optimal portfolios under such performance measures and realistic constraints leads to non-convex problems, we suggest to solve the problem with a heuristic method called Threshold Accepting (TA). TA is a very flexible technique as it requires no simplifications of the problem and allows for a straightforward implementation of all kinds of constraints. Applying this algorithm to actual data, we find that TA is well-adapted to optimisation problems of this type. Furthermore, we show that the computations can easily be distributed which leads to considerable speedups.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1156355_code623849.pdf?abstractid=1156355&mirid=1