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dc.contributor.advisorPaul D. Sclavounos.en_US
dc.contributor.authorLi, Derek Zen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.date.accessioned2012-02-29T18:22:55Z
dc.date.available2012-02-29T18:22:55Z
dc.date.copyright2011en_US
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/69516
dc.descriptionThesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 36).en_US
dc.description.abstractForecasting the future prices or returns of a security is extraordinarily difficult if not impossible. However, statistical analysis of a basket of highly correlated securities offering a cross-sectional representation of a particular sector can yield information that is potentially tradable. Securities related to the fossil fuels industry are used as the basis of a practical application to two distinct forecasting techniques. The first method, forecasting using conditional multivariate Gaussian statistics, was shown to yield, in a relative sense, the best results for those securities which exhibited a high correlation with the rest of the basket. For the second method, principal component analysis was done on a basket of commodity futures to reveal a small number of dominant factors governing the movements of the portfolio. Autoregressive models were then applied to both the factors and futures, but results showed both to be essentially Markov processes.en_US
dc.description.statementofresponsibilityby Derek Z. Li.en_US
dc.format.extent36 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleStatistical analysis of correlated fossil fuel securitiesen_US
dc.typeThesisen_US
dc.description.degreeS.B.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.identifier.oclc775781166en_US


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