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Shrinking the Variance-Covariance Matrix: Simpler is Better

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dc.contributor.author Muhammad Husnain
dc.contributor.author Arshad Hassan
dc.contributor.author Eric Lamarque
dc.date.accessioned 2016-09-07T10:13:53Z
dc.date.available 2016-09-07T10:13:53Z
dc.date.issued 2016-06
dc.identifier.uri http://hdl.handle.net/123456789/14806
dc.description 21 : 1 (Summer 2016): pp. 1–21 en_US
dc.description.abstract This study focuses on the estimation of the covariance matrix as an input to portfolio optimization. We compare 12 covariance estimators across four categories – conventional methods, factor models, portfolios of estimators and the shrinkage approach – applied to five emerging Asian economies (India, Indonesia, Pakistan, the Philippines and Thailand). We find that, in terms of the root mean square error and risk profile of minimum variance portfolios, investors gain no additional benefit from using the more complex shrinkage covariance estimators over the simpler, equally weighted portfolio of estimators in the sample countries. en_US
dc.language.iso en en_US
dc.publisher © Lahore School of Economics en_US
dc.relation.ispartofseries Vol.21;No.1
dc.subject Variance-Covariance Matrix en_US
dc.subject Mean-Variance Criteria en_US
dc.subject Portfolio Management en_US
dc.title Shrinking the Variance-Covariance Matrix: Simpler is Better en_US
dc.type Book en_US


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