Optimization with Performance-Attribution Constraints

W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu and Abootaleb Shirvani
Additional contact information
W. Brent Lindquist: Texas Tech University
Svetlozar T. Rachev: Texas Tech University
Yuan Hu: University of California San Diego
Abootaleb Shirvani: Kean University

Chapter Chapter 11 in Advanced REIT Portfolio Optimization, 2022, pp 181-196 from Springer

Abstract: Abstract How well a portfolio performs is of primary concern for investors and governs investor confidence in the portfolio’s management. Attribution analysis provides measures for how well a portfolio is being managed. While performance-attribution measures have been used traditionally as a diagnostic tool, this chapter introduces the recent development to include these measures as constraints in portfolio optimization. Two such measures, asset allocation and the selection effect, are used to constrain conditional value-at-risk optimization of the domestic REIT portfolio under historical and dynamic optimization. The results are analyzed in terms of price and reward-to-risk performance measures. Performance improvement is then characterized in terms of the attribution measure used as the constraint, the optimization method, and the level of turnover constraint.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (4)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:dymchp:978-3-031-15286-3_11

Ordering information: This item can be ordered from
http://www.springer.com/9783031152863

DOI: 10.1007/978-3-031-15286-3_11

Access Statistics for this chapter

More chapters in Dynamic Modeling and Econometrics in Economics and Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().