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Non-Linear Exchange Rate Relationships: An Automated Model Selection Approach with Indicator Saturation

Author

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  • Josh R. Stillwagon

    (Department of Economics, Trinity College)

Abstract

This paper examines whether the explanatory power of exchange rate models can be improved by allowing for cross-country asymmetries and non-linear effects of fundamentals. Both appear to be crucial. The data set looks at the USD versus pound and yen exchange rates from 1982:07-2012:02, and bias-corrected automated model selection is conducted with indicator saturation. Several non-linear effects are significant at the 1% level including for exchange rate momentum and Taylor rule effects of fundamentals. In many cases, larger changes in fundamentals lead to changes in the exchange rate at an increasing rate. Additionally, most of the indicators present in the linear models are eliminated once allowing for non-linearities, suggesting some of the structural breaks and outliers found in previous work were an artifact of the misspecified linear functional form.

Suggested Citation

  • Josh R. Stillwagon, 2014. "Non-Linear Exchange Rate Relationships: An Automated Model Selection Approach with Indicator Saturation," Working Papers 1405, Trinity College, Department of Economics.
    • Handle: RePEc:tri:wpaper:1405
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    References listed on IDEAS

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    5. Charles Engel & Nelson C. Mark & Kenneth D. West, 2008. "Exchange Rate Models Are Not as Bad as You Think," NBER Chapters, in: NBER Macroeconomics Annual 2007, Volume 22, pages 381-441, National Bureau of Economic Research, Inc.
    6. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May.
    7. Goldberg, Michael D & Frydman, Roman, 2001. "Macroeconomic Fundamentals and the DM/$ Exchange Rate: Temporal Instability and the Monetary Model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 421-435, October.
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    Citations

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    Cited by:

    1. Salisu, Afees A. & Ndako, Umar B., 2018. "Modelling stock price–exchange rate nexus in OECD countries: A new perspective," Economic Modelling, Elsevier, vol. 74(C), pages 105-123.
    2. Yao, Can-Zhong & Lin, Qing-Wen, 2017. "Recurrence plots analysis of the CNY exchange markets based on phase space reconstruction," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 584-596.
    3. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2023. "Robust Discovery of Regression Models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 31-51.
    4. Roman Frydman & Joshua R. Stillwagon, 2016. "Stock-Market Expectations: Econometric Evidence that both REH and Behavioral Insights Matter," Working Papers Series 44, Institute for New Economic Thinking.
    5. Natalia Ponomareva & Jeffrey Sheen & Ben Zhe Wang, 2019. "The common component of bilateral US exchange rates: to what is it related?," Empirical Economics, Springer, vol. 56(4), pages 1251-1268, April.
    6. Frydman, Roman & Stillwagon, Joshua R., 2018. "Fundamental factors and extrapolation in stock-market expectations: The central role of structural change," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 189-198.

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    More about this item

    Keywords

    Exchange Rates; determination puzzle; non-linearities; cross-country asymmetries; automated model selection; indicator saturation;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange

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