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Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques

Theophilos Papadimitriou (), Periklis Gogas () and Vasilios Plakandaras ()

No 5-2013, DUTH Research Papers in Economics from Democritus University of Thrace, Department of Economics

Abstract: 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.

Keywords: International Financial Markets; Foreign Exchange; Support Vector Regression; Monetary exchange rate models (search for similar items in EconPapers)
JEL-codes: F30 F31 G15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-mon
Date: 2013-11-07
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