ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)

Testing Exchange Rate Models in a Small Open Economy: an SVR Approach

Theophilos Papadimitriou, Periklis Gogas and Vasilios Plakandaras

Correspondence: Vasilios Plakandaras,

Department of Economics, Democritus University of Thrace, Greece

pdf (691.15 Kb) | doi:


We empirically test the validity of four popular monetary exchange rate models under five alternative inflation expectation approximations using the NOK/USD exchange rate. The selection of Norway seems appropriate as it is a small open economy that does not participate in most economic or political organizations and uses the Government Pension Fund as a tool to dampen external shocks to the domestic economy. The main innovation of the paper is that in addition to a standard VECM model used in the literature, we employ a two-step procedure for the first time in this setting; first, we train a Support Vector Regression (SVR) model and then we extract the coefficients through a Dynamic Evolving Neural Fuzzy Inference System (DENFIS). The best overall model in terms of fitting the phenomenon is an SVR one with autoregressive inflation expectations that exclude energy prices, exhibiting four times lower forecasting error than the best VECM model. The estimated coefficients of the VECM are not statistically significant, while the ones from the SVR-DENFIS model show that the sign of the coefficient on the interest rate differential corroborates only with the model proposed by Bilson (1978), while we detect a significant inflation rate differential. We conclude that fundamentals possess adequate forecasting ability when used in exchange rate forecasting but none of the tested monetary exchange rate models can explicitly describe the evolution path of the exchange rate. Nevertheless, the proposed machine learning methodology moves one step further than the econometric approach in tackling the exchange rate disconnect puzzle.


  Exchange Rate, Forecasting, Foreign Exchange Market, Support Vector Regression, Monetary exchange rate models.


Abhyankar, Abhay & Sarno, Lucio & Valente, Giorgio (2005). Exchange rates and fundamentals: evidence on the economic value of predictability, Journal of International Economics, 66, 325-348.

Akaike, H. (1969), Fitting autoregressive models for prediction. Annals of the Institute of Statistical. Mathematics, vol. 21, pp. 243-247.

Akram, Q. F. (2004). Oil prices and exchange rates: Norwegian evidence. The Econometrics Journal, 7, 476–504.

Backus, David, Allan Gregory, and Chris Telmer (1993). Accounting for Forward Rates in Markets for Foreign Currency. Journal of Finance, 48, 1887–1908.

Bilson, J. (1978). The monetary approach to the exchange rate-some empirical evidence. IMF Staff Papers. 25, 48–75.

Cheung,Y.-W., Chinn M. and Pascual A.G. (2005) Empirical Exchange Rate Models of the Nineties: Are any Fit to Survive?, Journal of International Money and Finance, 24, 1150–75.

Chinn, L., Azali, M., & Matthews, G. (2007a). The monetary approach to exchange rate determination for Malaysia. Applied Financial Economic Letters, 3, 91–94.

Chinn, L., Azali, M., Yusop, Z. B., & Yusoff, M. B. (2007b). The monetary model of exchange rate: evidence from the Philippines. Applied Economic Letters, 14, 993– 997.

Cortes C. and Vapnik V. (1995) Support-Vector Networks, Machine Learning, vol 20, pp. 273-297.

Cowles A. (1933), Can Stock Market Forecasts Forecast, Econometrica, vol. 1, pp. 309-324.

Cushman, D. (2007). A portfolio balance approach to the Canadian–U.S. exchange rate. Review of Financial Economics, 16, 305–320.

De Grauwe, P. (1996).International money: Postwar-trends and theories, New York: Oxford: Oxford University Press, 146–147.

Della Corte, Pasquale & Sarno, Lucio & Tsiakas, Ilias (2011). Spot and forward volatility in foreign exchange, Journal of Financial Economics, 100, 496-513.

Dornbusch, R. (1976). Expectations and exchange rate dynamics. Journal of Political Economy, 84, 1161–1176

Engel C. and West K. (2005), Exchange Rates and Fundamentals, Journal of Political Economy, 113, 485-517.

Farquad M., Ravi V. and Bapi Raju S, (2010), Support vector regression based hybrid rule extraction methods for forecasting, Expert Systems with Applications: An International Journal, 37, 5577-5589.

Frankel, J. A. (1979). On the mark: a theory of floating exchange rates based on real interest differentials. American Economic Review, vol. 69, pp. 610–622.

Frenkel, J. A. (1976). A monetary approach to the exchange rate: doctrinal aspects and empirical evidence. The Scandinavian Journal of Economics, 78, 200–224

Frenkel, M., & Koske, I. (2004). How well can monetary factors explain the exchange rate of the euro? Atlantic Economic Journal, vol. 32, 232–243.

Hannan, E. J., and B. G. Quinn (1979). The Determination of the Order of an Autoregression, Journal of the Royal Statistical Society, B, vol.41, pp.190–195.

Härdle, Wolfgang, Yuh‐Jye Lee, Dorothea Schäfer, and Yi‐Ren Yeh (2009) Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies, Journal of Forecasting, vol.28(6), pp. 512-534.

Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inferences on cointegration—with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169–210.

Kasabov N.K. and Song Qun (2002), Dynamic evolving neuro-fuzzy inference system (DENFIS), IEEE Transactions on Fuzzy Systems, 10, 144.

Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo (2010), Consumer credit-risk models via machine-learning algorithms, Journal of Banking & Finance, vol. 34(11), pp. 2767-2787.

Kloster, A. (2000). Estimating and interpreting interest rate expectations. Norges Economic Bulletin, LXXI, 85–94.

Loria, E., Sanchez, A., & Salgado, U. (2009). New evidence on the monetary approach of exchange rate determination in Mexico 1994–2007: a cointegrated SVAR model. Journal of International Money and Finance, vol. 29, 540–554.

Mark, N., and D. Sul (2001), Nominal Exchange Rates and Monetary Fundamentals: Evidence from a Small Post-Bretton Woods Sample, Journal of International Economics, vol. 53, pp. 29-52.

Meese, R. and Rogoff K. (1983) Empirical Exchange Rate Models of the Seventies: Do they Fit out of Sample?, Journal of International Economics, vol. 14, pp. 3–24.

Miyakoshi, T. (2000). The monetary approach to the exchange rate: empirical observations from Korea. Applied Economics Letters, 7, 791–794.

Neyman, J. and Pearson, E. S. (1933). On the Problem of the Most Efficient Tests of Statistical Hypotheses. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering SciencesI, vol. 231, pp. 694–706.

Obstfeld M. and Rogoff K. (2000) The Six Major Puzzles in International Macroeconomics: Is there a Common Cause, NBER Working paper series, no.7777.

Öğüt, Hulisi, M. Mete Doğanay, Nildağ Başak Ceylan, and Ramazan Aktaş (2012), Prediction of bank financial strength ratings: The case of Turkey." Economic Modelling, vol. 29(3), pp. 632-640.

Papadamou S. and Markopoulos T. (2012), The Monetary Approach to the Exchange Rate Determination for a “Petrocurrency”: The Case for Norwegian Krone, International Advances in Economic Research, vol. 18, pp. 299-314.

Papadimitriou, T., Gogas, P., Matthaiou, M., & Chrysanthidou, E. (2014 forthcoming). Yield curve and Recession Forecasting in a Machine Learning Framework. Computational Economics.

Plakandaras V., Papadimitriou T., Gogas P. and Gupta R. (2014 forthcoming) Forecasting the Real U.S. House Prices Index, Economic Modelling.

Politis D. and Romano J. (1994), The Stationary Bootstrap, Journal of the American Statistical Association, vol. 89, pp. 1303-1313.

Politits D, White H. and Patton A. (2009), Correction: Automatic Block-Length Selection for the Dependent Bootstrap, Econometric Reviews, vol. 28(4), pp. 372-375.

Rime D., Sarno L. and Sojli E. (2010), Exchange rate forecasting, order flow and macroeconomic information, Journal of International Economics, vol. 80, pp. 72- 88.

Rubio, Ginés, Héctor Pomares, Ignacio Rojas, and Luis Javier Herrera (2011) A heuristic method for parameter selection in LS-SVM: Application to time series prediction, International Journal of Forecasting, vol. 27(3), pp. 725-739.

Schwarz, Gideon E. (1978). Estimating the dimension of a model. Annals of Statistics, vol. 6 (2), pp. 461–464.

Svensson, L. E. O. (1994).Estimating and interpreting forward rates: Sweden 1992-1994. No. 94/114 Working Paper Series, International Monetary Fund.

Vapnik, V., Boser, B. and Guyon, I. (1992) A training algorithm for optimal margin classifiers, Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, ACM, pp.144–152

White, Halbert (2000), A Reality Check for Data Snooping, Econometrica, vol. 68(5), pp. 1097-1126.