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Modelling Exchange Rate Returns Using Non-linear Models

Author

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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)

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.

Suggested Citation

  • Manish Kumar, 2010. "Modelling Exchange Rate Returns Using Non-linear Models," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(1), pages 101-125, January.
    • Handle: RePEc:sae:mareco:v:4:y:2010:i:1:p:101-125
    DOI: 10.1177/097380100900400105
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    References listed on IDEAS

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    More about this item

    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;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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