Modelling Exchange Rate Returns Using Non-linear Models

Manish Kumar
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Manish Kumar: IREVNA, a division of CRISIL, Chennai, India; he is also a Ph.D. Research Scholar at the Department of Management Studies, Indian Institute of Technology, Madras, India; e-mail: manishkumar_iitm@yahoo.co.in

Margin: The Journal of Applied Economic Research, 2010, vol. 4, issue 1, 101-125

Abstract: Forecasting exchange rate movements is challenging, as they exhibit high volatility, complexity and noise. Most traditional models cannot forecast exchange rates, with significantly higher accuracy, than a random walk model. In this study, a non-linear model called artificial neural network (ANN) is used to forecast short-term (daily and weekly) movement of United States dollar (USD)/Japanese yen (JPY). ANN’s out-of-sample performance is benchmarked against the traditional Autoregressive Integrated Moving Average (ARIMA) model. Performance of both models is rigorously evaluated using three different penalty-based criteria: Directional Accuracy (DA), Correct Upward (CU) and Correct Downward (CD) trends and two non-penalty-based criteria: mean square error (MSE) and normalised mean square error (NMSE). Moreover, the robustness of the two models is tested for different sampling periods. Empirical results show that ANN per-forms better than ARIMA and delivered consistent results across all periods tested. This supports ANN’s robustness and also the fact that it can be used to formulate a strategy for trading in USD/JPY.

Keywords: Currency Exchange Rate; Artificial Neural Network; ARIMA; Forecasting; Time Series Analysis; JEL Classification: C22; JEL Classification: C45; JEL Classification: C52; JEL Classification: F31; JEL Classification: F37 (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:sae:mareco:v:4:y:2010:i:1:p:101-125

DOI: 10.1177/097380100900400105

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