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Gaussian Estimation of Long-Range Dependent Volatility in Asset Prices

Author

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  • Paolo Zaffaroni

Abstract

Asset returns have a very complicated dynamic pattern. Yet they display regularity across different assets and periods. We consider a new family of volatility models which account for such patterns, focussing in particular on the long memory nature of asset returns volatility. We propose an estimation procedure for such models based on a Gaussian pseudo maximum likelihood estimator, for which we establish the relevant asymptotic theory. An empirical application based on forex and stock return indexes suggests the potential of these models to capture the dynamic features of the data.

Suggested Citation

  • Paolo Zaffaroni, 1997. "Gaussian Estimation of Long-Range Dependent Volatility in Asset Prices," STICERD - Econometrics Paper Series 329, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:329
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    Cited by:

    1. 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.
    2. Bollerslev, Tim & Wright, Jonathan H., 2000. "Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data," Journal of Econometrics, Elsevier, vol. 98(1), pages 81-106, September.

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