IDEAS home Printed from https://ideas.repec.org/a/jof/jforec/v21y2002i7p513-42.html
   My bibliography  Save this article

The Performance of Non-linear Exchange Rate Models: A Forecasting Comparison

Author

Listed:
  • Boero, Gianna
  • Marrocu, Emanuela

Abstract

In recent years there has been a considerable development in modelling nonlinearities and asymmetries in economic and financial variables. The aim of the current paper is to compare the forecasting performance of different models for the returns of three of the most traded exchange rates in terms of the U.S. dollar, namely the French franc (FF/$), the German mark (DM/$) and the Japanese yen (Y/$). The relative performance of non-linear models of the SETAR, STAR and GARCH types is contrasted with their linear counterparts. The results show that if attention is restricted to mean square forecast errors, the performance of the models, when distinguishable, tends to favor the linear models. The forecast performance of the models is evaluated also conditional on the regime at the forecast origin and on density forecasts. This analysis produces more evidence of forecasting gains from non-linear models. Copyright © 2002 by John Wiley & Sons, Ltd.

Suggested Citation

  • Boero, Gianna & Marrocu, Emanuela, 2002. "The Performance of Non-linear Exchange Rate Models: A Forecasting Comparison," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 513-542, November.
  • Handle: RePEc:jof:jforec:v:21:y:2002:i:7:p:513-42
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F, 2002. "The Properties Of Some Goodness-Of-Fit Tests," The Warwick Economics Research Paper Series (TWERPS) 653, University of Warwick, Department of Economics.
    2. Emekter, Riza & Jirasakuldech, Benjamas & Snaith, Sean M., 2009. "Nonlinear dynamics in foreign exchange excess returns: Tests of asymmetry," Journal of Multinational Financial Management, Elsevier, vol. 19(3), pages 179-192, July.
    3. G. Ascari & E. Marrocu, 2003. "Forecasting inflation: a comparison of linear Phillips curve models and nonlinear time serie models," Working Paper CRENoS 200307, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    4. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    5. Michael Funke & Marc Gronwald, 2008. "The Undisclosed Renminbi Basket: Are the Markets Telling Us Something about Where the Renminbi-US Dollar Exchange Rate is Going?," The World Economy, Wiley Blackwell, vol. 31(12), pages 1581-1598, December.
    6. Wiśniewska Marta, 2014. "Eurusd Intraday Price Reversal," Folia Oeconomica Stetinensia, De Gruyter Open, vol. 14(2), pages 152-162, December.
    7. Boero, Gianna & Marrocu, Emanuela, 2004. "The performance of SETAR models: a regime conditional evaluation of point, interval and density forecasts," International Journal of Forecasting, Elsevier, vol. 20(2), pages 305-320.
    8. Armin Shmilovici & Yoav Kahiri & Irad Ben-Gal & Shmuel Hauser, 2009. "Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 33(2), pages 131-154, March.
    9. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
    10. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    11. Rossen Anja, 2016. "On the Predictive Content of Nonlinear Transformations of Lagged Autoregression Residuals and Time Series Observations," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(3), pages 389-409, May.
    12. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    13. G. Boero & E. Marrocu, 2001. "Evaluating non-linear models on point and interval forecasts: an application with exchange rate returns," Working Paper CRENoS 200110, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    14. Huber Florian, 2016. "Forecasting exchange rates using multivariate threshold models," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(1), pages 193-210, January.
    15. Hyeyoen Kim, 2011. "Large Data Sets, Nonlinearity and the Speed of Adjustment to Real Exchange Rate Shocks," Post-Print hal-00665456, HAL.
    16. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    17. Nan Cai & Zongwu Cai & Ying Fang & Qiuhua Xu, 2015. "Forecasting major Asian exchange rates using a new semiparametric STAR model," Empirical Economics, Springer, vol. 48(1), pages 407-426, February.
    18. Gianna Boero & Emanuela Marrocu, 2005. "Evaluating non-linear models on point and interval forecasts: an application with exchange rates," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 58(232), pages 91-120.
    19. Grier, Kevin B. & Smallwood, Aaron D., 2013. "Exchange rate shocks and trade: A multivariate GARCH-M approach," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 282-305.
    20. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, Elsevier.
    21. Yoon, Gawon, 2010. "Do real exchange rates really follow threshold autoregressive or exponential smooth transition autoregressive models?," Economic Modelling, Elsevier, vol. 27(2), pages 605-612, March.
    22. Sitzia, Bruno & Iovino, Doriana, 2008. "Nonlinearities in Exchange rates: Double EGARCH Threshold Models for Forecasting Volatility," MPRA Paper 8661, University Library of Munich, Germany.
    23. repec:ibn:ijefaa:v:10:y:2018:i:2:p:150-160 is not listed on IDEAS

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:21:y:2002:i:7:p:513-42. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.