International Journal of Computer Science and Application (IJCSA)

Editor-in-Chief: Prof. Avireni Srinivasulu
Frequency: Quarterly
ISSN Online: 2324-7134
ISSN Print: 2324-7037
RSS
Paper Infomation

Fuzzy Systems Neural Networks and Makov Switching AR Model for Prediction of Exchange Rates

Full Text(PDF, 273KB)

Author: A.F.M. Khodadad Khan, Mohammed Anwer, Shipra Banik

Abstract: Many international agents (e.g. money managers, investment banks, investors, funds makers and others) are very concerned about predicted values of exchange rates because it often moves dustically and generally affects the profits. This paper forecasted the daily Bangladeshi and Canadian exchange rates for the period of October 1996 to January 2013. With attention paid to recently developed econometric noises, the widely-used forecasting model the fuzzy extension of artificial neural network is considered and compared results with the Markov switching autoregressive forecasting model. Root mean square error and correlation coefficient are used as an evaluation measures. It has been found that the fuzzy extension of the artificial neural network model is a superior predictor compared to the other selected predictor for the Bangladeshi series and the reverse observed for the Canadian series. It is believed that the findings of this paper will be helpful for multinational organizations wanting to make wise policy about these two country’s exchange rates.

Keywords: Exchange Rate Dynamics, Time Series Prediction, Non-Linearities, Econometric Noises, Aritificial Intelligence, Markov Switching Model



References:

[1] Banik, S. “Testing for Stationarity, Seasonality and Long Memory in Economic and Financial Time Series”. Unpublished Ph.D. thesis, School of Business, La Trobe University, Australia, 1999.

[2] Banik, S. and Silvapulle, P. “Testing for Seasonal Stability in Unemployment Series: International Evidence”, Empirica, Springer, 26(2), 123-139, 1999.

[3] Banik, S., Chanchary, F.H., Khan, K., Rouf, R.A. and Anwer, M. “Neural Network and Genetic Algorithm Approaches for Forecasting Bangladeshi Monsoon Rainfall”. International Technology Management Review, vol. 2(1), 1-18, 2009.

[4] Box, G.E.P. and Jenkins, G.M. (1970), Time Series Analysis: Forecasting and Control, San Francisco : Holden-Day.

[5] Dueker, M. and Neely, C.J. “Can Markov Switching Models Predict Excess Foreign Exchange Returns?”. Journal of Banking and Finance, vol.31, 279-296, 2007.

[6] Engel, C. and Hamilton, J.D. “Long Swings in the Dollar: Are They in the Data and do Markets Know It?”.American Economic Review, vol.80, 689-713, 1990.

[7] Greene, W.H., Econometric Analysis, Prentice Hall, seventh edition, Upper Saddle River, NJ, 2008.

[8] Hamilton, J.D. “A New Approach to the Economic Analysis of Non-Stationary Time Series and the Business Cycle”. Econometrica, vol.57, 357-384, 1989.

[9] Huang, Y. “The Persistence of Taiwan's Output Fluctuations: An Empirical Study Using Innovation Regime-Switching Model”. Applied Economics, vol.39, 2673-2679, 2007.

[10] Ismail, M.T. and Isa, Z. ”Modeling Exchange Rates Using Regime Switching Model”. Sains Malaysiana, vol.35, 55-62, 2006.

[11] Jang, J.S.R. “ANFIS: Adaptive-network-based Fuzzy Inference Systems”. IEEE Transactions on Systems, Man, and Cybernetics, 665-685, 1993.

[12] Kodogiannis, V. and Lolis, A. “Forecasting Financial Time Series Using Neural Network and Fuzzy System-Based Techniques”. Neural Computing and Applications, vol.11, 90-102, 2002.

[13] Kuan, C.M. and Liu, T., “Forecasting Exchange Rates Usingfeed forward and Recurrent Neural Networks”. Journal of Applied Econometrics, vol.10, 347-364, 1995.

[14] Lisi, F. and Schiavo, R.A.“A comparison between Neural Networks and Chaotic Models for Exchange Rate Prediction”.Computational Statistics and Data Analysis, vol.30, 87-102, 1999.

[15] Mitchell, W.F. “Testing for Unit Roots and Persistence in OECD Unemployment Rates”. Applied Economics,vol.25, 1489-1501, 1999.

[16] Nelson, C.R. and Plosser, C.I. “Trends and Random Walks in Macroeconomics Time Series”. Journal of Monetary Economics, 10, 139-162, 1982.

[17] Phillips, P.C.B. and Perron, P., Testing for Unit Root in Time Series Regression, Biometrica, 79, 335-346, 1988.

[18] Ping-Feng, W.C.H., Chih-Shen, P. L. and Chen, C.T. “A Hybrid Support Vector Machine Regression for Exchange Rate Prediction”. Information and Management Sciences, vol.17, 19-32, 2006.

[19] Tae, H.H. and Steurer, E. “Exchangr Rate Forecasting: Neural Networks vs. Linear Models Using Monthly and Weekly Data”. Neurocomputing, vol.10, 323-339, 1995.

[20] Said, S.E. and Dickey, D.A. “Testing for Unit Roots in Autoregressive Moving Average Models of Unknown Order”, Biometrica, 71, 599-607,1984.

[21] Zhang, G. and Hu, M.Y. ”Neural Network Forecasting of the

[22] British Pound/US Dollar Exchange Rate”. International Journal of Management Science, vol.26, 495-506, 1998.