Common Factor Augmented Forecasting Models for the US Dollar-Korean Won Exchange Rate

53 Pages Posted: 18 Feb 2020

Date Written: February 14, 2020


We propose factor-augmented out of sample forecasting models for the real exchange rate between Korea and the US. We estimate latent common factors by applying an array of data dimensionality reduction methods to a large panel of monthly frequency time series data. We augment benchmark forecasting models with common factor estimates to formulate out-of-sample forecasts of the real exchange rate. Major findings are as follows. First, our factor models outperform conventional forecasting models when combined with factors from the US macroeconomic predictors. Second, our factor models perform well at longer horizons when American real activity factors are employed, whereas American nominal/financial market factors help improve short-run prediction accuracy. Third, models with global PLS factors from UIP fundamentals overall perform well, while PPP and RIRP factors play a limited role in forecasting.

Keywords: Won/Dollar Real Exchange Rate, Principal Component Analysis, Partial Least Squares, LASSO, Out-of-Sample Forecast

JEL Classification: C38, C53, C55, F31, G17

Suggested Citation

Kim, Hyeongwoo and Kim, Soohyon, Common Factor Augmented Forecasting Models for the US Dollar-Korean Won Exchange Rate (February 14, 2020). Bank of Korea WP 2020-5, Available at SSRN: or

Hyeongwoo Kim (Contact Author)

Auburn University ( email )

138 Miller
Auburn University
Auburn, AL 36849
United States
+1-334-844-2928 (Phone)

HOME PAGE: http://

Soohyon Kim

The Bank of Korea ( email )

39, Namdaemun-ro, Jung-gu
Seoul, 04531
Korea, Republic of (South Korea)

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