The Effects of International F/X Markets on Domestic Currencies Using Wavelet Networks: Evidence from Emerging Markets

Cifter, Atilla and Ozun, Alper (2007): The Effects of International F/X Markets on Domestic Currencies Using Wavelet Networks: Evidence from Emerging Markets.

Abstract

This paper proposes a powerful methodology wavelet networks to investigate the effects of international F/X markets on emerging markets currencies. We used EUR/USD parity as input indicator (international F/X markets) and three emerging markets currencies as Brazilian Real, Turkish Lira and Russian Ruble as output indicator (emerging markets currency). We test if the effects of international F/X markets change across different timescale. Using wavelet networks, we showed that the effects of international F/X markets increase with higher timescale. This evidence shows that the causality of international F/X markets on emerging markets should be tested based on 64-128 days effect. We also find that the effects of EUR/USD parity on Turkish Lira is higher on 17-32 days and 65-128 days scales and this evidence shows that Turkish lira is less stable compare to other emerging markets currencies as international F/X markets effects Turkish lira on shorten time scale.

• Item Type: MPRA Paper
• Institution: Marmara University
• Language: English
• Keywords: F/X Markets; Emerging markets; Wavelet networks; Wavelets; Neural networks
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; F - International Economics > F3 - International Finance > F31 - Foreign Exchange; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets

References

Alsberg, B.K., A.M. Woodward, and D.B. Kell, 1997. An Introduction to Wavelet Transforms for Chemometricians: A Time-Frequency Approach, Chemometrics and Intelligent Laboratory Systems, 37, 215-239.

Campbell, C., 1997, Constructive Learning Techniques for Designing Neural Network Systems, in (ed CT Leondes) Neural Network Systems Technologies and Applications. San Diego: Academic Press.

Carney J.G., Cunningham P., 1999. The NeuralBAG algorithm: Optimizing generalization performance in bagged neural networks, to be presented at 7th European Symposium on Artificial Neural Networks, Bruges (Belgium), 21-23, April 1999.

Daugman, J., 1988. Complete discrete 2D Gabor transform by neural networks for image analysis and compression, IEEE Trans. Acoustics, Speech, and Signal Processing, 36, 1169-1179.

Dickey, D.A., Fuller,W.A., 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 1057–1072.

Echauz J. and Vachtsevanos G., 1996, Elliptic and radial wavelet neural networks in Proceding of Second World Automation Congress, Montpellier, France, 5, 173-179.

Engle, R.F., Granger, C.W.J., 1987. Co-integration and error correction: Representation, estimation, and testing. Econometrica 55, 251–276.

Gallegati, M., 2005. A Wavelet Analysis of MENA Stock Markets, Mimeo, Universita Politecnica Delle Marche, Ancona, Italy

Gencay, R., 1999. Linear, non-linear and essential foreign exchange rate prediction with some simple technical trading rules. Journal of International Economics Vol. 47, pp. 91–107

Gencay, R., Selcuk, F., and Whitcher, B., 2002. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press

Gilbert, E.W., Krishnaswamy, C.R., Pashley, M.M., 2000. Neural Network Applications in Finance: A Practical Introduction.

Johansen, Søren, 1991.Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica 59, 1551–1580.

Johansen, Søren, 1995. Likelihood-based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press.

Hertz, J. Anders Krogh, and Richard G. Palmer, 1991. Introduction to the Theory of Neural Computing. Addison-Wesley.

Hu, M. Y., G. Q. Zhang, C. Z. Jiang, & B. E. Patuwo, 1999. A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting, Decision Sciences, 30/1, 197-216.

Jamal, A.M.M. and C. Sundar, 1997. Modeling Exchange Rate Changes with Neural Networks. Journal of Applied Business Research 14 /1, 1-5.

Muzy, J.-F., D. Sornette, J. Delour and A. Arneodo, 2001, Multifractal returns and Hierarchical Portfolio Theory, Quantitative Finance Vol. 1/1, pp. 131-148.

Müller, U. A, M.M. Dacorogna, R.B. Olsen, O.V. Pictet, and J.E. von Weizsacker, 1995. Volatilities of Different Time Resolutions - Analyzing the Dynamics of Market Components, The First International Conference on High Frequency Data in Finance, Zurich, March.

Ozun, A., 2006. Theoretical Importance of Artificial Neural Networks For The Efficiency of Financial Markets, Proceeding of 5th International Finance Sypmosium: Integration in the Financial Markets, Vienna University and Marmara University, 613-622, 25-26 May, 2006, Istanbul,.

Percival, D.B., and Walden, A.T., 2000. Wavelet Methods for Time Series Analysis, Cambridge University Press

Procházka, A., V. Sýs, 1994, Time Series Prediction Using Genetically Trained Wavelet Networks. In Neural Networks for Signal Processing 3 - Proceedings of the 1994 IEEE Workshop, Ermioni, Greece, 1994. IEEE SP Society.

Ramer, A. and V. Kreinovich, 1994. Maximum entropy approach to fuzzy control. Information Sciences, 81/3, 235-260.

Rumelhart, D. and McClelland, J., 1986. Parallel Distributed Processing, MIT Press, Cambridge, MA.

Sin., T. and Han, I., 2000, A Hybrid System Using Multiple Cyclic Decomposition Methods and Neural Network Techniques for Point Forecast, Decision Making, Proceedings of the 33rd Hawaii International Conference on System Sciences

Szu, H., Telfer, B., Kadambe, S., 1992. Neural network adaptive wavelets for signal representation and classification, Opt. Engineering, 31, 1907-1916.

Tkacz, G., 2001, Estimating the Fractional Order of Integration of Interest Rates Using a Wavelet OLS Estimator”, Studies in Nonlinear Dynamics&Econometrics, Vol. 5, Issue 1, ss. 19-32

Tang, Z., C. Almeida, and P.A. Fishwick, 1991, Time Series Forecasting Using Neural Networks vs Box-Jenkins Methodology, Simulation, 57/5, 303-310

Yao, J.T., H.-L. Poh, T. Jasic, 1996. Foreign exchange rates forecasting with neural networks, Proceedings of the International Conference on Neural Information Processing, Hong Kong, September 1996, 754-759.

Zhang M., 1992. Study of the Inference Engine and User Interface of the Tool for Building Expert Systems with Object-Oriented Technology, Masters Thesis, Artificial Intelligence Research Centre, the Agricultural University of Hebei, Baoding, China.