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Sparse Weighted-Norm Minimum Variance Portfolios
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OA
AI
DOI：10.1093/rof/rfv024.png)
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摘要
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We propose to impose a weighted l(1) and squared l(2) 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.
Keyword:
NAIVE DIVERSIFICATION
MARKOWITZ
SELECTION
OPTIMIZATION
LASSO
期刊
IF:
8.4
论文数:
904
被引数:
4.8K
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