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Likelihood inference in BL-GARCH models

Author

Listed:
  • Giuseppe Storti
  • Cosimo Vitale

Abstract

The paper presents a procedure based on the EM algorithm for the indirect estimation of the parameters of BiLinear GARCH (BL-GARCH) models. BL-GARCH generalize the class of GARCH models by considering interactions of past shocks and volatilities in the conditional variance equation. In this way the response of the conditional variance to past information becomes asymmetric allowing to account for the so called leverage effect, typically characterizing the behaviour of financial time series. The results of an application to a time series of stock market returns are presented. Copyright Physica-Verlag 2003

Suggested Citation

  • Giuseppe Storti & Cosimo Vitale, 2003. "Likelihood inference in BL-GARCH models," Computational Statistics, Springer, vol. 18(3), pages 387-400, September.
    • Handle: RePEc:spr:compst:v:18:y:2003:i:3:p:387-400
    DOI: 10.1007/BF03354605
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    References listed on IDEAS

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    Cited by:

    1. Abdou Kâ Diongue & Dominique Guegan & Rodney C. Wolff, 2008. "Exact Maximum Likelihood estimation for the BL-GARCH model under elliptical distributed innovations," Post-Print halshs-00270719, HAL.
    2. Giuseppe Storti & Cosimo Vitale, 2003. "BL-GARCH models and asymmetries in volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 19-39, February.
    3. Abdou Kâ Diongue & Dominique Guegan & Rodney C. Wolff, 2010. "BL-GARCH model with elliptical distributed innovations," Post-Print halshs-00368340, HAL.
    4. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.

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