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Long Memory and Forecasting in Euroyen Deposit Rates

Author

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  • John Barkoulas

    (Boston College)

  • Christopher F. Baum

    (Boston College)

Abstract

We test for long memory in 3- and 6-month daily returns series on Eurocurrency deposits denominated in Japanese yen (Euroyen). The fractional differencing parameter is estimated using the spectral regression method. The conflicting evidence obtained from the application of tests against a unit root as well as tests against stationarity provides the motivation for testing for fractional roots. Significant evidence of positive long-range dependence is found in the Euroyen returns series. The estimated fractional models result in dramatic out-of-sample forecasting improvements over longer horizons compared to benchmark linear models, thus providing strong evidence against the martingale model. Series: Boston College Working Papers in Economics

Suggested Citation

  • John Barkoulas & Christopher F. Baum, 1997. "Long Memory and Forecasting in Euroyen Deposit Rates," Boston College Working Papers in Economics 361, Boston College Department of Economics.
    • Handle: RePEc:boc:bocoec:361
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    References listed on IDEAS

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    Cited by:

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    2. Mulligan, Robert F. & Koppl, Roger, 2011. "Monetary policy regimes in macroeconomic data: An application of fractal analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 201-211, May.
    3. Mulligan, Robert F., 2004. "Fractal analysis of highly volatile markets: an application to technology equities," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(1), pages 155-179, February.
    4. Mulligan, Robert F. & Lombardo, Gary A., 2004. "Maritime businesses: volatile stock prices and market valuation inefficiencies," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(2), pages 321-336, May.
    5. Burcu Kıran, 2012. "Nonlinearity and Fractional Integration in the US Dollar/Euro Exchange Rate," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 59(3), pages 325-334, June.
    6. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    7. Selçuk BAYRACI, 2017. "Long-memory, self-similarity and scaling of the long-term government bond yields: Evidence from Turkey and the USA," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(3(612), A), pages 71-82, Autumn.

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    More about this item

    Keywords

    long memory; ARFIMA processes; spectral regression; unit roots; forecasting;
    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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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