IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v30y2005i3p643-663.html
   My bibliography  Save this article

Genetic multi-model composite forecast for non-linear prediction of exchange rates

Author

Listed:
  • Marcos Álvarez-Díaz
  • Alberto Álvarez

Abstract

No abstract is available for this item.

Suggested Citation

  • Marcos Álvarez-Díaz & Alberto Álvarez, 2005. "Genetic multi-model composite forecast for non-linear prediction of exchange rates," Empirical Economics, Springer, vol. 30(3), pages 643-663, October.
  • Handle: RePEc:spr:empeco:v:30:y:2005:i:3:p:643-663
    DOI: 10.1007/s00181-005-0249-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00181-005-0249-5
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00181-005-0249-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.
    2. Clements, Kenneth W. & Lan, Yihui, 2010. "A new approach to forecasting exchange rates," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1424-1437, November.
    3. Jying-Nan Wang & Jiangze Du & Chonghui Jiang & Kin-Keung Lai, 2019. "Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    7. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    8. Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    10. Alvarez-Diaz, Marcos & Caballero Miguez, Gonzalo, 2008. "The quality of institutions: A genetic programming approach," Economic Modelling, Elsevier, vol. 25(1), pages 161-169, January.
    11. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2021. "“Nowcasting and forecasting GDP growth with machine-learning sentiment indicators”," AQR Working Papers 202101, University of Barcelona, Regional Quantitative Analysis Group, revised Feb 2021.

    More about this item

    Keywords

    Composite-forecast or data-fusion; genetic programming; neural networks; exchange-rate forecasting; C14; C53; G14;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:spr:empeco:v:30:y:2005:i:3:p:643-663. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.