Conditional asset allocation using prediction intervals to produce allocation decisions

B Blair ()
Additional contact information
B Blair: WestLB Asset Management

Journal of Asset Management, 2002, vol. 2, issue 4, No 3, 325-335

Abstract: Abstract Traditional conditional asset allocation involves using key past economic and financial data to produce forecasts of expected returns for the various asset classes involved in the asset allocation decision. Ordinarily these are point forecasts and little or no use is made of the prediction interval in the decision-making process. The main reason is that forecast models are misspecified, hence error distributions are unknown and so model-based prediction intervals provide spurious measures of forecast uncertainty. Alternative approaches, such as Bayesian densities and bootstrapping, provide non-parametric forecast distributions, which are not model based and so reduce the uncertainty about the nature of the prediction interval. Here we investigate various different prediction intervals and attempt to quantify biases between model-based and non-model-based solutions. Asset allocation decisions based on prediction densities are investigated, and it is found that knowledge of a more accurate prediction interval is economically meaningful in an asset allocation context.

Keywords: asset allocation; bootstrapping; prediction densities; kernel regression (search for similar items in EconPapers)
Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1057/palgrave.jam.2240056 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:pal:assmgt:v:2:y:2002:i:4:d:10.1057_palgrave.jam.2240056

Ordering information: This journal article can be ordered from
http://www.springer.com/finance/journal/41260

DOI: 10.1057/palgrave.jam.2240056

Access Statistics for this article

Journal of Asset Management is currently edited by Marielle de Jong and Dan diBartolomeo

More articles in Journal of Asset Management from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().