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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/79288

    Title: Sparse Weighted Norm Minimum Variance Portfolios
    Authors: 顏佑銘
    Yen, Yu-Min
    Contributors: 國貿系
    Date: 2015-06
    Issue Date: 2015-11-02 17:19:41 (UTC+8)
    Abstract: We propose to impose a weighted l1 and squared l2 norm penalty on the portfolio weights to improve out-of-sample (OOS) performances of portfolio optimization when the number of assets becomes large. We show that under certain conditions, the realized risk of the optimal minimum variance portfolio (MVP) obtained from the strategy can asymptotically be lower than those of benchmark portfolios with a high probability. Our theoretical results imply that penalty parameters for the weighted-norm penalty can be specified as a simple function of the number of assets and sample size. With the theoretical results, we also develop an automatic calibration procedure for choosing the penalty parameters. We demonstrate superior OOS performances of the weighted-norm MVP with two real data sets. Finally, we propose several alternative norm penalties and show that their OOS performances are comparable to the weighted-norm strategy.
    Relation: Review of Finance
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1093/rof/rfv024
    DOI: 10.1093/rof/rfv024
    Appears in Collections:[科技管理與智慧財產研究所] 期刊論文

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