Quantile aggregation and combination for stock return prediction

Chuanliang Jiang, Esfandiar Maasoumi and Zhijie Xiao

Econometric Reviews, 2020, vol. 39, issue 7, 715-743

Abstract: Model averaging for forecasting and mixed estimation is a recognized improved statistical approach. This paper is a first report on: (1). aggregate information from different conditional quantiles within a given model and, (2). model averaging with quantile averaging. Based on a subset of possible methods, we show that aggregating information over different quantiles, with and without combining information across different models, can produce superior forecasts, outperforming forecasts based on conditional mean regressions. We observe a variety of quantile aggregation schemes within a model can significantly improve over forecasts obtained from model combination alone. We provide simulation and empirical evidence. In addition economic value of our proposals is demonstrated within an optimal portfolio decision setting. Higher values of average utility are observed with no exception when an investor employs forecasts which aggregate both within and across model information.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2020.1771902 (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:emetrv:v:39:y:2020:i:7:p:715-743

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2020.1771902

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().