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Contaminated Variance–Mean mixing model

Author

Listed:
  • Fung, Thomas
  • Wang, Joanna J.J.
  • Seneta, Eugene

Abstract

The Generalised Normal Variance–Mean (GNVM) model in which the mixing random variable is Gamma distributed is considered. This model generalises the popular Variance-Gamma (VG) distribution. This GNVM model can be interpreted as the addition of noise to a (skew) VG base. The discussion is based on goodness of fit criteria and on parameter estimation. The conclusion is that the shape of the VG distribution can be adjusted in a favourable way by adding noise.

Suggested Citation

  • Fung, Thomas & Wang, Joanna J.J. & Seneta, Eugene, 2013. "Contaminated Variance–Mean mixing model," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 258-267.
    • Handle: RePEc:eee:csdana:v:67:y:2013:i:c:p:258-267
    DOI: 10.1016/j.csda.2013.05.024
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    References listed on IDEAS

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    1. Richard Finlay & Eugene Seneta, 2008. "Stationary‐Increment Variance‐Gamma and t Models: Simulation and Parameter Estimation," International Statistical Review, International Statistical Institute, vol. 76(2), pages 167-186, August.
    2. S. James Press, 1967. "A Compound Events Model for Security Prices," The Journal of Business, University of Chicago Press, vol. 40, pages 317-317.
    3. Thomas Fung & Eugene Seneta, 2010. "Modelling and Estimation for Bivariate Financial Returns," International Statistical Review, International Statistical Institute, vol. 78(1), pages 117-133, April.
    4. Madan, Dilip B & Seneta, Eugene, 1990. "The Variance Gamma (V.G.) Model for Share Market Returns," The Journal of Business, University of Chicago Press, vol. 63(4), pages 511-524, October.
    Full references (including those not matched with items on IDEAS)

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