Optimal Diversification within Mixed-Asset Portfolios using a Conditional Heteroskedasticity Approach: Evidence from the U.S. and the U.K

Michael Giliberto, Foort Hamelink, Martin Hoesli and Bryan MacGregor

Journal of Real Estate Portfolio Management, 1999, vol. 5, issue 1, 31-45

Abstract: Executive Summary. In this article, portfolio allocation strategies based on a threshold autoregressive conditional heteroskedasticity model (QTARCH) are constructed for the United States and the United Kingdom and compared to a conventional asset allocation. Our procedure is based on partitioning the historical data into ‘states of the world,’ which are used to produce expectations of return and risk. Several approaches are developed to partition an initial in-sample period (1978-1983), using quarterly asset returns and economic data. These partitions are then used to test out-of-sample strategies for the next quarter. Although the conditional results are sensitive to the method of partitioning, we show that the approach can improve portfolio performance in both countries and that most of the performance improvement stems from using conditional variances-covariances.

Date: 1999
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10835547.1999.12089565 (text/html)
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:taf:repmxx:v:5:y:1999:i:1:p:31-45

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/repm20

DOI: 10.1080/10835547.1999.12089565

Access Statistics for this article

Journal of Real Estate Portfolio Management is currently edited by Peng Liu and Vivek Sah

More articles in Journal of Real Estate Portfolio Management from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().