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

Mir, Zulfiqar Ali (2025): Penalized regression methods for exchange rate forecasting: evidence from the U.S. dollar index.

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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.

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