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| Content Provider | Springer Nature Link |
|---|---|
| Author | Branda, Martin Kopa, Miloš |
| Copyright Year | 2013 |
| Abstract | In this paper, several concepts of portfolio efficiency testing are compared, based either on data envelopment analysis (DEA) or the second-order stochastic dominance (SSD) relation: constant return to scale DEA models, variable return to scale (VRS) DEA models, diversification-consistent DEA models, pairwise SSD efficiency tests, convex SSD efficiency tests and full SSD portfolio efficiency tests. Especially, the equivalence between VRS DEA model with binary weights and the SSD pairwise efficiency test is proved. DEA models equivalent to convex SSD efficiency tests and full SSD portfolio efficiency tests are also formulated. In the empirical application, the efficiency testing of 48 US representative industry portfolios using all considered DEA models and SSD tests is presented. The obtained efficiency sets are compared. A special attention is paid to the case of small number of the inputs and outputs. It is empirically shown that DEA models equivalent either to the convex SSD test or to the SSD portfolio efficiency test work well even with quite small number of inputs and outputs. However, the reduced VRS DEA model with binary weights is not able to identify all the pairwise SSD efficient portfolios. |
| Starting Page | 13 |
| Ending Page | 35 |
| Page Count | 23 |
| File Format | |
| ISSN | 1435246X |
| Journal | Central European Journal of Operations Research |
| Volume Number | 22 |
| Issue Number | 1 |
| e-ISSN | 16139178 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2013-01-11 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Data envelopment analysis Second-order stochastic dominance Pairwise SSD efficiency Convex SSD efficiency SSD portfolio efficiency Operations Research/Decision Theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Management Science and Operations Research |
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