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Nonlinear Time Series With Long Memory: A Model for Stochastic Volatility

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  • Paolo Zaffaroni
  • Peter M. Robinson

Abstract

We introduce a nonlinear model of stochastic volatility within the class of product type models. It allows different degrees of dependence for the raw series and for the squared series, for instance implying weak dependence in the former and long memory in the latter. We discuss its main statistical properties with respect to the common set of stylized facts characterizing financial assets returns time series dynamics, and apply it to several series of asset returns.

Suggested Citation

  • Paolo Zaffaroni & Peter M. Robinson, 1997. "Nonlinear Time Series With Long Memory: A Model for Stochastic Volatility," FMG Discussion Papers dp253, Financial Markets Group.
  • Handle: RePEc:fmg:fmgdps:dp253
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    File URL: http://www.lse.ac.uk/fmg/workingPapers/discussionPapers/fmg_pdfs/dp253.pdf
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    Cited by:

    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-059, New York University, Leonard N. Stern School of Business-.
    2. Meddahi, N., 2001. "An Eigenfunction Approach for Volatility Modeling," Cahiers de recherche 2001-29, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. Arteche, Josu, 2004. "Gaussian semiparametric estimation in long memory in stochastic volatility and signal plus noise models," Journal of Econometrics, Elsevier, vol. 119(1), pages 131-154, March.
    4. F. DePenya & L. Gil-Alana, 2006. "Testing of nonstationary cycles in financial time series data," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 47-65, August.
    5. Ana Pérez & Esther Ruiz, 2002. "Modelos de memoria larga para series económicas y financieras," Investigaciones Economicas, Fundación SEPI, vol. 26(3), pages 395-445, September.

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