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Are Exchange Rates Disconnected from Macroeconomic Variables? Evidence from the Factor Approach

Listed author(s):
  • Yunjung Kim

    ()

    (Department of Economics, Korea University, Seoul, Republic of Korea)

  • Cheolbeom Park

    ()

    (Department of Economics, Korea University, Seoul, Republic of Korea)

We use factor-augmented predictive regression to analyze the relation between nominal exchange rates and macroeconomic variables. Using a panel of 127 US macroeconomic time series, we estimate eight factors through principal component analysis. Those estimated factors have significant predictive power and can substantially improve the predictive power of Purchasing Power Parity through both in-sample and out-of-sample analyses. The estimated macroeconomic factor, which comoves with US money supply measures, has strong predictive power for nominal exchange rate fluctuations in the short run, while estimated factors, comoving with interest rate spreads and employment variables, have strong predictive power in the long run. Moreover, optimal factors selected by the BIC in the out-of-sample analysis differ greatly depending on the time points when forecasts are made. Finally, we show that factors extracted from a panel of 127 US time series data and those extracted from a panel of 215 Korean macroeconomic series together can predict a substantial portion of movements in the Korea-US bilateral exchange rate.

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File URL: http://econ.korea.ac.kr/~ri/WorkingPapers/w1606.pdf
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Paper provided by Institute of Economic Research, Korea University in its series Discussion Paper Series with number 1606.

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Date of creation: 2016
Handle: RePEc:iek:wpaper:1606
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  1. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
  2. Bacchetta, Philippe & van Wincoop, Eric, 2013. "On the unstable relationship between exchange rates and macroeconomic fundamentals," Journal of International Economics, Elsevier, vol. 91(1), pages 18-26.
  3. Moench, Emanuel, 2008. "Forecasting the yield curve in a data-rich environment: A no-arbitrage factor-augmented VAR approach," Journal of Econometrics, Elsevier, vol. 146(1), pages 26-43, September.
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  8. Park, Cheolbeom & Park, Sookyung, 2013. "Exchange rate predictability and a monetary model with time-varying cointegration coefficients," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 394-410.
  9. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
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  11. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
  12. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  13. Chinn, Menzie D. & Meese, Richard A., 1995. "Banking on currency forecasts: How predictable is change in money?," Journal of International Economics, Elsevier, vol. 38(1-2), pages 161-178, February.
  14. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-218, March.
  15. Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, 07.
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