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Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques

  • Theophilos Papadimitriou


    (Department of Economics, University Campus Komotini, Democritus University of Thrace, Greece)

  • Periklis Gogas


    (Department of Economics, University Campus Komotini, Democritus University of Thrace, Greece)

  • Vasilios Plakandaras


    (Department of Economics, University Campus Komotini, Democritus University of Thrace, Greece)

In this paper, we approximate the empirical findings of Papadamou and Markopoulos (2012) on the NOK/USD exchange rate under a Machine Learning (ML) framework. By applying Support Vector Regression (SVR) on a general monetary exchange rate model and a Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) to extract model structure, we test for the validity of popular monetary exchange rate models. We reach to mixed results since the coefficient sign of interest rate differential is in favor only with the model proposed by Bilson (1978), while the inflation rate differential coefficient sign is approximated by the model of Frankel (1979). By adopting various inflation expectation estimates, our SVR model fits actual data with a small Mean Absolute Percentage Error when an autoregressive approach excluding energy prices is adopted for inflation expectation. Overall, our empirical findings conclude that for a small open petroleum producing country such as Norway, fundamentals possess significant forecasting ability when used in exchange rate forecasting.

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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 59_13.

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Date of creation: Nov 2013
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Handle: RePEc:rim:rimwps:59_13
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  1. Cheung, Yin-Wong & Chinn, Menzie & Garcia Pascual, Antonio, 2003. "Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?," Santa Cruz Department of Economics, Working Paper Series qt5fc508pt, Department of Economics, UC Santa Cruz.
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  5. Frankel, Jeffrey A, 1979. "On the Mark: A Theory of Floating Exchange Rates Based on Real Interest Differentials," American Economic Review, American Economic Association, vol. 69(4), pages 610-22, September.
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  7. Lee Chin & M. Azali & K. G. Matthews, 2007. "The monetary approach to exchange rate determination for Malaysia," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 3(2), pages 91-94.
  8. Della Corte, Pasquale & Sarno, Lucio & Tsiakas, Ilias, 2011. "Spot and forward volatility in foreign exchange," Journal of Financial Economics, Elsevier, vol. 100(3), pages 496-513, June.
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  10. Frenkel, Jacob A, 1976. " A Monetary Approach to the Exchange Rate: Doctrinal Aspects and Empirical Evidence," Scandinavian Journal of Economics, Wiley Blackwell, vol. 78(2), pages 200-224.
  11. Cushman, David O., 2007. "A portfolio balance approach to the Canadian-U.S. exchange rate," Review of Financial Economics, Elsevier, vol. 16(3), pages 305-320.
  12. Giorgio Valente & Lucio Sarno & Abhay Abhayankar, 2004. "Exchange Rates and Fundamentals: Evidence on the Economic Value of Predictability," Working Papers wp04-01, Warwick Business School, Finance Group.
  13. Rime, Dagfinn & Sarno, Lucio & Sojli, Elvira, 2010. "Exchange rate forecasting, order flow and macroeconomic information," Journal of International Economics, Elsevier, vol. 80(1), pages 72-88, January.
  14. Stephanos Papadamou & Thomas Markopoulos, 2012. "The Monetary Approach to the Exchange Rate Determination for a “Petrocurrency”: The Case of Norwegian Krone," International Advances in Economic Research, International Atlantic Economic Society, vol. 18(3), pages 299-314, August.
  15. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
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