Penalized regression methods for exchange rate forecasting: evidence from the U.S. dollar index

Zulfiqar Ali Mir

MPRA Paper from University Library of Munich, Germany

Abstract: This paper examines the effectiveness of penalized regression techniques in forecasting exchange rate movements. Using daily data for the U.S. Dollar Index (DXY) in 2016, we compare the performance of Ordinary Least Squares (OLS) with Ridge and Lasso regression models. The predictors include gold and silver returns, the S&P 500 Index, short- and long-term Treasury yields, and the EURUSD exchange rate. Results show that while OLS suffers from instability due to multicollinearity, Ridge regression improves coefficient stability and predictive accuracy. Lasso regression provides the best overall performance, with the highest explanatory power and the lowest prediction error, by selecting only the most relevant variables. These findings underscore the value of penalized regression in financial econometrics and highlight its potential for robust exchange rate forecasting.

Keywords: Penalized Regression; Ridge; Lasso; Exchange Rate Forecasting; Dollar Index; Financial Econometrics; Machine Learning in Finance (search for similar items in EconPapers)
JEL-codes: C01 C55 C58 F31 F37 F47 G12 G15 G17 (search for similar items in EconPapers)
Date: 2025-09-01
New Economics Papers: this item is included in nep-for
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/125996/1/MPRA_paper_125996.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/126014/1/MPRA_paper_125996.pdf revised version (application/pdf)

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:pra:mprapa:125996

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().