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A Stochastic Optimization Approach to Financial Decision Making

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Koivu, Matti
dc.date.accessioned 2013-10-31T09:41:12Z
dc.date.available 2013-10-31T09:41:12Z
dc.date.issued 2004
dc.identifier.isbn 951-791-841-0
dc.identifier.issn 1237-556X
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/11213
dc.description.abstract Stochastic optimization is an effective tool for analyzing decision problems under uncertainty. In stochastic optimization a decision problem is formulated as an optimization problem, where the objective is to find an optimal decision, while considering all the possible scenarios for the uncertain factors and dependencies between the decision variables through time. In stochastic optimization the decision problem is solved numerically and there are only minor limitations for decision criteria, constraints and distributions of random factors that can be used in the formulations. This thesis consists of an introductory section and four articles. The introduction summarizes the contents and findings of the four articles and provides an introduction to the main issues in stochastic optimization: formulation of the decision problem as a stochastic program, econometric modeling of the stochastic factors and discretization of the problem for numerical solution. The first two articles are related to Asset-Liability Management (ALM) problem of a Finnish pension company. Article 1 develops a stochastic model for assets and liabilities of a pension company. The model is used for producing long term forecasts for asset returns as well as company’s liabilities and cash-flows. The model is utilized in Article 2, where a stochastic optimization model for ALM of a Finnish pension company is developed. The model is used as a decision support tool for finding long-term dynamic investment decisions in an uncertain environment, where the aim is to cover the uncertain future liabilities with dynamic investment strategies. The last two Articles address the problem of discretization of stochastic programs for numerical solution. New scenario generation techniques based on deterministic and randomized integration quadratures, more precisely Quasi Monte Carlo methods, are developed and applied to financial portfolio optimization problems. Conditions that guarantee the convergence of the objectives and solutions of the discretized problems to the original one are derived for both, Quasi-Monte Carlo and Randomized Quasi-Monte Carlo methods en
dc.format.extent [118] s.
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Helsinki School of Economics en
dc.publisher Helsingin kauppakorkeakoulu fi
dc.relation.ispartofseries Acta Universitatis oeconomicae Helsingiensis. A fi
dc.relation.ispartofseries 234 fi
dc.title A Stochastic Optimization Approach to Financial Decision Making en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Kauppakorkeakoulu fi
dc.contributor.school School of Business en
dc.identifier.urn URN:ISBN:951-791-841-0
dc.type.dcmitype text en
dc.programme.major Liikkeenjohdon systeemit fi
dc.programme.major Management Science en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.supervisor Kallio, Markku, professor
dc.opn Ziemba, William T., professor, University of British Columbia, USA
dc.subject.helecon Rahoitusmarkkinat
dc.subject.helecon Päätöksenteko
dc.subject.helecon Ekonometria
dc.subject.helecon Mallit
dc.subject.helecon Financial markets
dc.subject.helecon Decision making
dc.subject.helecon Econometrics
dc.subject.helecon Models
dc.date.defence 2004-05-25
dc.dissid 257
dc.identifier.bibid 603538
local.aalto.digifolder Aalto_68311
local.aalto.digiauth ask


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