A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation
Christian Pierdzioch,
Marian Risse and
Sebastian Rohloff
Applied Economics Letters, 2016, vol. 23, issue 5, 347-352
Abstract:
We use a boosting algorithm to forecast the returns of gold and silver prices. We then study the implications of using different information criteria to terminate the boosting algorithm in terms of the statistical and economic performance of a forecasting model. Our findings demonstrate that information criteria that select parsimonious forecasting models perform better in statistical terms than information criteria that select relatively complex forecasting models, but this good performance does not necessarily survive an economic performance evaluation.
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)
Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2015.1073835 (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:apeclt:v:23:y:2016:i:5:p:347-352
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20
DOI: 10.1080/13504851.2015.1073835
Access Statistics for this article
Applied Economics Letters is currently edited by Anita Phillips
More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().