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

Hyeongwoo Kim () and Soohyon Kim ()
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Soohyon Kim: Bank of Korea

No 2020-5, Working Papers from Economic Research Institute, Bank of Korea

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C38 C53 C55 F31 G17 (search for similar items in EconPapers)
Pages: 53 pages
Date: 2020-02-14
New Economics Papers: this item is included in nep-for and nep-ore
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