Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework

Tamal Datta Chaudhuri, Indranil Ghosh


Any discussion on exchange rate movements and forecasting should include explanatory variables from both the current account and the capital account of the balance of payments. In this paper, we include such factors to forecast the value of the Indian rupee vis a vis the US Dollar. Further, factors reflecting political instability and lack of mechanism for enforcement of contracts that can affect both direct foreign investment and also portfolio investment, have been incorporated. The explanatory variables chosen are the 3 month Rupee Dollar futures exchange rate (FX4), NIFTY returns (NIFTYR), Dow Jones Industrial Average returns (DJIAR), Hang Seng returns (HSR), DAX returns (DR), crude oil price (COP), CBOE VIX (CV) and India VIX  (IV). To forecast the exchange rate, we have used two different classes of frameworks namely, Artificial Neural Network (ANN) based models and Time Series Econometric models. Multilayer Feed Forward Neural Network (MLFFNN) and Nonlinear Autoregressive models with Exogenous Input (NARX) Neural Network are the approaches that we have used as ANN models. Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedastic (EGARCH) techniques are the ones that we have used as Time Series Econometric methods. Within our framework, our results indicate that, although the two different approaches are quite efficient in forecasting the exchange rate, MLFNN and NARX are the most efficient.

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Andreou, A. S. and Zombanakis, G. A. (2006), ‘Computational Intelligence in Exchange-Rate Forecasting’, Bank of Greece Working Paper, No. 49.

Bhat, S. A. and Nain, Md. Z. (2014) ‘Modelling the Conditional Heteroscedasticity and Leverage Effect in the BSE Sectoral Indices’, IUP Journal of Financial Risk Management, Vol. 11, pp. 49-61.

Bollerslev, T. (1986) ‘Generalized Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics, Vol. 31, pp. 307-327.

Caves, R. E. and Jones, R. W. (1981), ‘World Trade and Payments – An Introduction’, 3rd Edition, Little Brown and Company, Boston, Toronto.

Datta Chaudhuri, T. and Ghosh, I. (2015) ‘Forecasting Volatility in Indian Stock Market Using Artificial Neural Network with Multiple Inputs and outputs’, International Journal of Computer Applications, Vol. 120, pp. 7-15.

Dua, P. and Ranjan, R. (2011) ‘Modelling and Forecasting the Indian Re/Us Dollar Exchange Rate’, RBI Working Paper, No. 197.

Engle, R. F. (1982) ‘Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation’, Econometrica, Vol. 50, pp. 987-1008.

Gao, Y. and Meng, J. E. (2005) ‘NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches’, Fuzzy Sets and Systems, Vol. 150, No. 2, 2005, pp.331-350.

Garg, A. (2012) ‘Forecasting exchange rates using machine learning models with time-varying volatility’, Master Thesis,

Han, J., Kamber, M. and Pei, J. (2012) ‘Data Mining Concepts and Techniques’, 3rd Edition, Morgan Kauffman Publishers, USA.

Haykin, S. (1999), ‘Neural Networks’, Second Edition, Pearson Education.

Imam, T. (2012) ‘Intelligent Computing and Foreign Exchange Rate Prediction: What We Know and We Don’t’, Progress in Intelligent Computing and Applications, Vol. 1, pp. 1-15.

Jena, P. R., Majhi, R. and Majhi, B. (2015) ‘Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate prediction’, Journal of King Saud University - Computer and Information Sciences, Vol. 27, pp. 450-457.

Lam, L., Fung, L. and Yu, I. W. (2008) ‘Comparing Forecast Performance of Exchange Rate Models’, Hong Kong Monetary Authority Working Paper, No. 08.

Lin, T., Horne, B., G., Tino, P. and Giles, C. L. (1996) ‘Learning long-term dependencies in NARX recurrent neural networks’, IEEE Transactions on Neural Networks, Vol. 7, No. 6, pp. 1329-1351.

Lin, S. Y., Chen, C. H. and Lo, C. C. (2013) ‘Currency Exchange Rates Prediction based on Linear Regression Analysis Using Cloud Computing’, International Journal of Grid and Distributed Computing, Vol. 6, pp. 1-10.

Majhi, B., Rout, M., Majhi, R., Panda, G. and Fleming, P. J. (2012) ‘New robust forecasting models for exchange rates prediction’, Expert Systems with Applications, Vol. 39, pp.12658 - 12670.

Messe, R. A. and Rogoff K. K. (1983) ‘Empirical Exchange Rate Models of the Seventies Do they fit out of samples’, Journal of International Economics, Vol. 14, No. , pp. 3-24.

Nelson, D. B. (1991) ‘Conditional Heteroskedasticity in Asset Returns: A New Approach’, Econometrica, Vol. 59, pp. 347–370.

Pacelli, V. (2012) ‘Forecasting Exchange Rates: a Comparative Analysis’, International Journal of Business and Social Science, Vol. 3, pp. 145-156.

Perwej, Y. and Perwej, A. (2012) ‘Forecasting of Indian Rupee (INR) / US Dollar (USD) Currency Exchange Rate Using Artificial Neural Network’, International Journal of Computer Science, Engineering and Applications, Vol.2, No.2, pp. 41-52.

Premanode, B. and Toumazou, C. (2013) ‘Improving prediction of exchange rates using Differential EMD’, Expert Systems with Applications, Vol. 40, pp. 377-384.

Rabemananjara, R. and Zakoian, J. M. (1993) ‘Threshold ARCH models and asymmetries in volatility’, Journal of Applied Econometrics, Vol. 8, pp. 31–49.

Ravi, V., Lal, R. and Kiran, N. R. (2012) ‘Foreign Exchange Rate Prediction using Computational Intelligence Methods’, International Journal of Computer Information Systems and Industrial Management Applications, Vol. 4, pp. 659-670.

Sentana, E. (1995) ‘Quadratic ARCH models’, Review of Economic Studies, Vol. 62, pp. 639–661.

Tripathy, S. and Rahaman, A. (2013) ‘Forecasting Daily Stock Volatility Using GARCH Model: A Comparison between BSE and SSE’, IUP Journal of Applied Finance, Vol. 19, pp. 71-83.

Vojinovic, Z. and Kecman, V. (2001) ‘A data mining approach to financial time series modelling and forecasting’, Intelligent Systems in Accounting, Finance and Management, Vol. 10, pp. 225-239.

Zhang, G. P. and Berardi, V. L. (2001) ‘Time series forecasting with neural network ensembles: an application for exchange rate prediction’, Journal of Operational Research Society, Vol. 52, pp. 652-664.


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