Statistically Efficient Construction of α-Risk-Minimizing Portfolio
We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. (2004), an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Asia University
2012-01-01
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| Series: | Advances in Decision Sciences |
| Online Access: | http://dx.doi.org/10.1155/2012/980294 |
| Summary: | We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. (2004), an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asymptotic normality are obtained. We apply the results of Hallin et al. (2008) to the problem of constructing α-risk-minimizing portfolios using residual signs and ranks and a general reference density. Monte Carlo simulations assess the performance of the proposed method. Empirical applications are also investigated. |
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| ISSN: | 2090-3359 2090-3367 |