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Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd

Author

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  • Corina SAMAN

    (Institute for Economic Forecasting, Romanian Academy)

Abstract

This paper aims to explore the forecasting accuracy of RON/USD exchange rate structural models with monetary fundamentals. I used robust regression approach for constructing robust neural models less sensitive to contamination with outliers and I studied its predictability on 1 to 6-month horizon against nonrobust linear and nonlinear regressions and, especially, random walk. The results show that robust model with low breakdown point improve the forecast accuracy of RW and AR models on 1- and 4-month horizon and performs better than RW at all time horizons.

Suggested Citation

  • Corina SAMAN, 2015. "Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-106, March.
    • Handle: RePEc:rjr:romjef:v::y:2015:i:1:p:93-106
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    References listed on IDEAS

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    More about this item

    Keywords

    exchange rate; forecasting; neural networks; outliers;
    All these keywords.

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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