IIASA RepositoryStochastic Optimization of a Multi-currency Bond PortfolioT.HuoponenauthorThis paper presents a stochastic optimization approach for the management of multi-currency government bond portfolio. This practical problem of optimal fund allocation is formulated as a linearly constrained two-stage model where the parameter values are not known with certainty but depend on future course of underlying stochastic economic variables. The model differs from the standard two-stage formulation as data for the second stage problem is uncertain as well. The objective is to maximize the expected utility of the market value of the portfolio at the end of the planning horizon. To solve the problem, we employ a stochastic quasigradient method by Ermoliev. For the optimality test, upper and lower estimates for the optimal objective function value are developed based on a given confidence level. According to initial numerical results, the convergence to a satisfactory near-optimal solution is considered sufficiently fast for a practical application.1994-08WP-94-098Monograph

For work being deposited by its own author: In self-archiving this collection of files and associated bibliographic metadata, I grant IIASA Repository the right to store them and to make them permanently available publicly for free on-line. I declare that this material is my own intellectual property and I understand that IIASA Repository does not assume any responsibility if there is any breach of copyright in distributing these files or metadata. (All authors are urged to prominently assert their copyright on the title page of their work.)

For work being deposited by someone other than its author: I hereby declare that the collection of files and associated bibliographic metadata that I am archiving at IIASA Repository) is in the public domain. If this is not the case, I accept full responsibility for any breach of copyright that distributing these files or metadata may entail.

Clicking on the deposit button indicates your agreement to these terms.