Statistical analysis of correlated fossil fuel securities
Name
775781166-MIT.pdf
Description
Full printable version
Size
4.15 MB
Format
Adobe PDF
Checksum (MD5)
671e6dfb4ed87033d4ef771911c11085
Author(s)
Li, Derek Z
Advisor(s)
Paul D. Sclavounos.
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
Forecasting 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.
Description
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 36).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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