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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/71616

    Title: Solving Norm Constrained Portfolio Optimization via Coordinate-Wise Descent Algorithms
    Authors: 顏佑銘
    Yen, Yu-Min;Yen, Tso-Jung
    Contributors: 國貿系
    Keywords: Minimum variance portfolio;Weighted norm constraint;Berhu penalty;Grouped portfolio selection
    Date: 2014-08
    Issue Date: 2014-11-24 14:16:43 (UTC+8)
    Abstract: A fast method based on coordinate-wise descent algorithms is developed to solve portfolio optimization problems in which asset weights are constrained by lqlq norms for 1≤q≤21≤q≤2. The method is first applied to solve a minimum variance portfolio (mvp) optimization problem in which asset weights are constrained by a weighted l1l1 norm and a squared l2l2 norm. Performances of the weighted norm penalized mvp are examined with two benchmark data sets. When the sample size is not large in comparison with the number of assets, the weighted norm penalized mvp tends to have a lower out-of-sample portfolio variance, lower turnover rate, fewer numbers of active constituents and shortsale positions, but higher Sharpe ratio than the one without such penalty. Several extensions of the proposed method are illustrated; in particular, an efficient algorithm for solving a portfolio optimization problem in which assets are allowed to be chosen grouply is derived.
    Relation: Computational Statistics and Data Analysis, 76, 737-759
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.csda.2013.07.010
    DOI: 10.1016/j.csda.2013.07.010
    Appears in Collections:[國際經營與貿易學系 ] 期刊論文

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