Sparse Weighted-Norm Minimum Variance Portfolios

Yu-Min Yen

Review of Finance, 2016, vol. 20, issue 3, 1259-1287

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.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1093/rof/rfv024 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:revfin:v:20:y:2016:i:3:p:1259-1287.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Review of Finance is currently edited by Marcin Kacperczyk

More articles in Review of Finance from European Finance Association Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().