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Does Artificial Neural Network Forecast Better for Excessively Volatile Currency Pairs?

Sarveshwar Kumar Inani, Manas Tripathi, Saurabh Kumar

Abstract


This study predicts the exchange rates for three currency pairs (USD-INR, GBP-INR, and EUR-INR). We have used multi-layer perceptron (MLP) neural network architecture based on feed-forward with back-propagation learning method.  The sample of the study covers daily data for the period from January 2009 to January 2016. The findings of the study confirm that the neural network predicts better for more volatile currency pairs (GBP-INR and EUR-INR) as compared to a less volatile currency pair (USD-INR). The study further observes that the optimal forecast horizon for the neural network model should be equal to the optimal lag length used in the construction of the model. This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays a crucial role in the macro-economy of a country. Hence, prediction of currency exchange rate becomes imperative for various stakeholders such as government, the central bank, and investors to maximize the returns and minimize the risk in their decision-making.


Keywords


Artificial Neural Network, Excess Volatility, Currency Pairs, Forecasting

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References


Atiya, A.F. (2001), “Bankruptcy prediction for credit risk using neural networks: A survey and new results”, IEEE Transactions on Neural Networks, Vol. 12 No. 4, pp. 929–935.

Bildirici, M., Alp, E.A. and Ersin, Ö.Ö. (2010), “TAR-cointegration neural network model: An empirical analysis of exchange rates and stock returns”, Expert Systems with Applications, Vol. 37 No. 1, pp. 2–11.

Bildirici, M. and Ersin, Ö.Ö. (2009), “Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange”, Expert Systems with Applications, Vol. 36 No. 4, pp. 7355–7362.

Brooks, C. (1996), “Testing for non-linearity in daily sterling exchange rates”, Applied Financial Economics, Vol. 6 No. 4, pp. 307–317.

Chen, A.-S., Leung, M.T. and Daouk, H. (2003), “Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index”, Computers & Operations Research, Vol. 30 No. 6, pp. 901–923.

Chiarella, C., Peat, M. and Stevenson, M. (1994), “Detecting and modelling nonlinearity in flexible exchange rate time series”, Asia Pacific Journal of Management, Vol. 11 No. 2, pp. 159–186.

Dhamija, A.K. and Bhalla, V.K. (2010), “Exchange rate forecasting: comparison of various architectures of neural networks”, Neural Computing and Applications, Vol. 20 No. 3, pp. 355–363.

Dunis, C.L., Laws, J. and Schilling, U. (2012), “Currency trading in volatile markets: Did neural networks outperform for the EUR/USD during the financial crisis 2007–2009?”, Journal of Derivatives & Hedge Funds, Vol. 18 No. 1, pp. 2–41.

Eiteman, D.K., Stonehill, A.I. and Moffett, M.H. (2012), Multinational Business Finance, Twelfth Edition., Pearson Education, Boston.

El Shazly, M.R. and El Shazly, H.E. (1997), “Comparing the forecasting performance of neural networks and forward exchange rates”, Journal of Multinational Financial Management, Vol. 7 No. 4, pp. 345–356.

Gradojevic, N. and Yang, J. (2006), “Non-linear, non-parametric, non-fundamental exchange rate forecasting”, Journal of Forecasting, Vol. 25 No. 4, pp. 227–245.

Grudnitski, G. and Osburn, L. (1993), “Forecasting S&P and gold futures prices: An application of neural networks”, Journal of Futures Markets, Vol. 13 No. 6, pp. 631–643.

Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H. and Wu, S. (2004), “Credit rating analysis with support vector machines and neural networks: a market comparative study”, Decision Support Systems, Vol. 37 No. 4, pp. 543–558.

Kayal, P. and Maheswaran, S. (2016), “Is USD-INR Really an Excessively Volatile Currency Pair?”, Journal of Quantitative Economics, pp. 1–14.

Khashei, M. and Bijari, M. (2010), “An artificial neural network (p, d, q) model for timeseries forecasting”, Expert Systems with Applications, Vol. 37 No. 1, pp. 479–489.

Khashei, M. and Bijari, M. (2011), “A novel hybridization of artificial neural networks and ARIMA models for time series forecasting”, Applied Soft Computing, Vol. 11 No. 2, pp. 2664–2675.

Kumar, P.R. and Ravi, V. (2007), “Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review”, European Journal of Operational Research, Vol. 180 No. 1, pp. 1–28.

Lendasse, A., de Bodt, E., Wertz, V. and Verleysen, M. (2000), “Non-linear financial time series forecasting - Application to the Bel 20 stock market index”, European Journal of Economic and Social Systems, Vol. 14 No. 1, pp. 81–91.

Leung, M.T., Chen, A.-S. and Daouk, H. (2000), “Forecasting exchange rates using general regression neural networks”, Computers & Operations Research, Vol. 27 No. 11–12, pp. 1093–1110.

Lisi, F. and Schiavo, R.A. (1999), “A comparison between neural networks and chaotic models for exchange rate prediction”, Computational Statistics & Data Analysis, Vol. 30 No. 1, pp. 87–102.

Ma, Y. and Kanas, A. (2000), “Testing for a nonlinear relationship among fundamentals and exchange rates in the ERM”, Journal of International Money and Finance, Vol. 19 No. 1, pp. 135–152.

Majhi, R., Panda, G. and Sahoo, G. (2009), “Efficient prediction of exchange rates with low complexity artificial neural network models”, Expert Systems with Applications, Vol. 36 No. 1, pp. 181–189.

Meese, R.A. and Rose, A.K. (1991), “An Empirical Assessment of Non-Linearities in Models of Exchange Rate Determination”, The Review of Economic Studies, Vol. 58 No. 3, pp. 603–619.

Özkan, F. (2013), “Comparing the forecasting performance of neural network and purchasing power parity: The case of Turkey”, Economic Modelling, Vol. 31, pp. 752–758.

Panda, C. and Narasimhan, V. (2007), “Forecasting exchange rate better with artificial neural network”, Journal of Policy Modeling, Vol. 29 No. 2, pp. 227–236.

Prakash, A. (2012), “Major Episodes of Volatility in the Indian Foreign Exchange Market in the Last Two Decades (1993-2013): Central Bank’s Response”, Vol. 33 No. 1 & 2, pp. 166–199.

Tkáč, M. and Verner, R. (2016), “Artificial neural networks in business: Two decades of research”, Applied Soft Computing, Vol. 38, pp. 788–804.

Wilson, R.L. and Sharda, R. (1994), “Bankruptcy prediction using neural networks”, Decision Support Systems, Vol. 11 No. 5, pp. 545–557.

Zhang, G. and Hu, M.Y. (1998), “Neural network forecasting of the British Pound/US Dollar exchange rate”, Omega, Vol. 26 No. 4, pp. 495–506.

Zhang, G.P. (2003), “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing, Vol. 50, pp. 159–175.

Zhang, G.P. and Qi, M. (2005), “Neural network forecasting for seasonal and trend time series”, European Journal of Operational Research, Vol. 160 No. 2, pp. 501–514.

Zhu, X., Wang, H., Xu, L. and Li, H. (2008), “Predicting stock index increments by neural networks: The role of trading volume under different horizons”, Expert Systems with Applications, Vol. 34 No. 4, pp. 3043–3054.




DOI: http://dx.doi.org/10.5750/jpm.v10i2.1252

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