Computational methods for selecting optimal financial investment strategies

This thesis focuses on empirical asset allocations problems. The nonconvex optimization problem arising from our models specification is solved by means of heuristic optimization methods. Three empirical applications of a particular heuristic, the Threshold Accepting method, are proposed. The first problem that we consider is the replication of the Credit Suisse/Tremont (CST) Hedge Fund Index using liquid instruments such as equities, commodities and bonds. Our specification yields portfolios appearing to be an attractive substitute to the tracked index. In the second application we explore whether an asset allocation approach to Foreign Exchange Market is profitable. Our approach dominates the benchmark portfolio and technical trading model, as well as being less volatile. In the last application, we incorporate Asset-Liability Management in the asset allocation decision. The model used is the multistage programming, which outperforms the other approaches such as mean-variance or minimum downside risk