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A risk perspective of estimating portfolio weights of the global minimum-variance portfolio
Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
Linnaeus University, School of Business and Economics, Department of Economics and Statistics.ORCID iD: 0000-0002-3623-5034
Jönköping University, Sweden.
2019 (English)In: AStA Advances in Statistical Analysis, ISSN 1863-8171, E-ISSN 1863-818X, p. 1-22Article in journal (Refereed) Epub ahead of print
Abstract [en]

The problem of how to determine portfolio weights so that the variance of portfolio returns is minimized has been given considerable attention in the literature, and several methods have been proposed. Some properties of these estimators, however, remain unknown, and many of their relative strengths and weaknesses are therefore difficult to assess for users. This paper contributes to the field by comparing and contrasting the risk functions used to derive efficient portfolio weight estimators. It is argued that risk functions commonly used to derive and evaluate estimators may be inadequate and that alternative quality criteria should be considered instead. The theoretical discussions are supported by a Monte Carlo simulation and two empirical applications where particular focus is set on cases where the number of assets (p) is close to the number of observations (n).

Place, publisher, year, edition, pages
Springer, 2019. p. 1-22
National Category
Probability Theory and Statistics
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-89303DOI: 10.1007/s10182-018-00349-7OAI: oai:DiVA.org:lnu-89303DiVA, id: diva2:1354746
Available from: 2019-09-26 Created: 2019-09-26 Last updated: 2019-09-26

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Holgersson, ThomasKarlsson, Peter S.Stephan, Andreas

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf