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Combining forecasts: some results on exchange and interest rates

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  • Monica Billio
  • Domenico Sartore
  • Carlo Toffano

Abstract

The aim of this work is to investigate whether the combination of forecasts plays an important role in the improvement of forecast accuracy Particular attention is paid to: (a) the methods of forecasting (the methods compared are neural networks, fuzzy logic, GARCH models, switching regime and chaotic dynamics); (b) combining the forecasts provided by the different methods. This work has also the aim of revising a short-term econometric forecast using a longer-term forecast. The revision process usually runs the opposite way (revision is made on a longer-term forecast using a short-term one to reflect the current available information, but it is not excluded that it is possible to proceed as described above. Daily data from the financial market is used. Some empirical applications on exchange and interest rates are given.

Suggested Citation

  • Monica Billio & Domenico Sartore & Carlo Toffano, 2000. "Combining forecasts: some results on exchange and interest rates," The European Journal of Finance, Taylor & Francis Journals, vol. 6(2), pages 126-145.
    • Handle: RePEc:taf:eurjfi:v:6:y:2000:i:2:p:126-145
    DOI: 10.1080/13518470050020806
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    References listed on IDEAS

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    1. repec:adr:anecst:y:1987:i:6-7:p:19 is not listed on IDEAS
    2. Clemon, Robert T & Winkler, Robert L, 1986. "Combining Economic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 39-46, January.
    3. Diebold, Francis X. & Pauly, Peter, 1990. "The use of prior information in forecast combination," International Journal of Forecasting, Elsevier, vol. 6(4), pages 503-508, December.
    4. Carlo Carraro & Domenico Sartore, 1987. "Square Root Iterative Filter: Theory and Applications to Econometric Models," Annals of Economics and Statistics, GENES, issue 6-7, pages 435-459.
    5. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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    Cited by:

    1. Chuanhua Wei & Chenping Du & Nana Zheng, 2020. "A Changing Weights Spatial Forecast Combination Approach with an Application to Housing Price Prediction," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(4), pages 1-11, April.
    2. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2013. "Time-varying combinations of predictive densities using nonlinear filtering," Journal of Econometrics, Elsevier, vol. 177(2), pages 213-232.
    3. Piotr Wdowiński & Aneta Zglińska-Pietrzak, 2005. "The Warsaw Stock Exchange Index WIG: Modeling and Forecasting," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), Acta Universitatis Lodziensis. Folia Oeconomica nr 192/2005 - Issues in Modeling, Forecasting and Decision-Making in Financial Markets, edition 1, volume 127, chapter 7, pages 115-127, University of Lodz.
    4. Piotr Wdowinski & Aneta Zglinska-Pietrzak, 2005. "The Warsaw Stock Exchange Index WIG: Modelling and Forecasting," CESifo Working Paper Series 1570, CESifo.

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