Computational methods for selecting optimal financial investment strategies

Diplôme Thèse Computational methods for selecting optimal financial investment strategies eng 2013-02-26 author Lula Jonela 522559 director Gilli Manfred 24803 10.13097/archive-ouverte/unige:26689 urn:nbn:ch:unige-266898 SES 796 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. Heuristic optimization techniques Threshold Accepting method Asset allocations Index tracking Technical trading model Asset-liability management Dewey 330 Dewey 330/650 9 Faculté d'économie et de management S false 260e1576-2867-4797-bc3d-8443bf682e59 Thesis Public these.pdf 2097473 application/pdf openAccess Faculté d'économie et de management

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Computational methods for selecting optimal financial investment strategies				2013	1,042	261

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Lula, Jonela