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Forecasting Financial Times Series with Generalized Long Memory Processes

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Advances in Quantitative Asset Management

Part of the book series: Studies in Computational Finance ((SICF,volume 1))

Abstract

In this paper, we present a parametric modelling of financial time series using long memory k-factor Gegenbauer processes suggested by Gray et al. (1989) and known in the statistical literature as generalized long memory processes. Using forecasting performances of this kind of processes and recent results on estimation theory we show how on an application to the monthly time series of exchange rates for the French franc against the US dollar, we can improve the efficiency of the long-term forecasting process.

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Author information

Authors and Affiliations

  1. Université Paris, 13 CNRS, UMR 7539, France

    Laurent Ferrara

  2. RATP, Département Commercial, LAC A73, 54 Quai de la Rapée, 75599, Paris Cedex 12, France

    Laurent Ferrara

  3. UPESA 6056 — CREST, Université de Reims, France

    Dominique Guégan

Authors
  1. Laurent Ferrara
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  2. Dominique Guégan
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Editor information

Editors and Affiliations

  1. Liverpool Business School, UK

    Christian L. Dunis

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© 2000 Springer Science+Business Media New York

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Ferrara, L., Guégan, D. (2000). Forecasting Financial Times Series with Generalized Long Memory Processes. In: Dunis, C.L. (eds) Advances in Quantitative Asset Management. Studies in Computational Finance, vol 1. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4389-3_14

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  • DOI: https://doi.org/10.1007/978-1-4615-4389-3_14

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-6974-5

  • Online ISBN: 978-1-4615-4389-3

  • eBook Packages: Springer Book Archive

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