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Application of robust statistics to asset allocation models

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dc.contributor.advisor Roy E. Welsch and George Verghese. en_US
dc.contributor.author Zhou, Xinfeng en_US
dc.contributor.other Massachusetts Institute of Technology. Operations Research Center. en_US
dc.date.accessioned 2007-02-21T13:10:50Z
dc.date.available 2007-02-21T13:10:50Z
dc.date.copyright 2006 en_US
dc.date.issued 2006 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/36231
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. en_US
dc.description Includes bibliographical references (p. 105-107). en_US
dc.description.abstract Many strategies for asset allocation involve the computation of expected returns and the covariance or correlation matrix of financial instruments returns. How much of each instrument to own is determined by an attempt to minimize risk (the variance of linear combinations of investments in these financial assets) subject to various constraints such as a given level of return, concentration limits, etc. The expected returns and the covariance matrix contain many parameters to estimate and two main problems arise. First, the data will very likely have outliers that will seriously affect the covariance matrix. Second, with so many parameters to estimate, a large number of observations are required and the nature of markets may change substantially over such a long period. In this thesis we use robust covariance procedures, such as FAST-MCD, quadrant-correlation-based covariance and 2D-Huber-based covariance, to address the first problem and regularization (Bayesian) methods that fully utilize the market weights of all assets for the second. High breakdown affine equivariant robust methods are effective, but tend to be costly when cross-validation is required to determine regularization parameters. en_US
dc.description.abstract (cont.) We, therefore, also consider non-affine invariant robust covariance estimation. When back-tested on market data, these methods appear to be effective in improving portfolio performance. In conclusion, robust asset allocation methods have great potential to improve risk-adjusted portfolio returns and therefore deserve further exploration in investment management research. en_US
dc.description.statementofresponsibility by Xinfeng Zhou. en_US
dc.format.extent 107 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.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.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Operations Research Center. en_US
dc.title Application of robust statistics to asset allocation models en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Operations Research Center. en_US
dc.identifier.oclc 77061489 en_US


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