Economics at your fingertips  

A real-time quantile-regression approach to forecasting gold returns under asymmetric loss

Christian Pierdzioch, Marian Risse and Sebastian Rohloff

Resources Policy, 2015, vol. 45, issue C, 299-306

Abstract: We propose a real-time quantile-regression approach to analyze whether widely studied macroeconomic and financial variables help to forecast out-of-sample gold returns. The real-time quantile-regression approach accounts for model uncertainty, model instability, and the possibility that a forecaster has an asymmetric loss function. Forecasts are computed and evaluated using the same asymmetric loss function. When the loss function implies that an underestimation is somewhat more costly than an overestimation of the same size, the forecasts computed using the real-time quantile-regression approach outperform forecasts implied by an autoregressive benchmark model.

Keywords: Quantile regression; Forecasting; Asymmetric loss; Gold returns (search for similar items in EconPapers)
JEL-codes: C53 E44 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

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:

Access Statistics for this article

Resources Policy is currently edited by R. G. Eggert

More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-04-01
Handle: RePEc:eee:jrpoli:v:45:y:2015:i:c:p:299-306