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Pricing and Inference with Mixtures of Conditionally Normal Processes

  • Henri Bertholon

    (Crest)

  • Alain Monfort

    (Crest)

  • Fulvio Pegoraro

    (Crest)

We consider the problems of derivative pricing and inference when the stochastic discount factor has an exponentialaffineform and the geometric return of the underlying asset has a dynamics characterized by a mixture of conditionallyNormal processes. We consider both the static case in which the underlying process is a white noise distributed as amixture of Gaussian distributions (including extreme risks and jump diffusions) and the dynamic case in which theunderlying process is conditionally distributed as a mixture of Gaussian laws. Semi-parametric, non parametric andSwitching Regime situations are also considered. In all cases, the risk-neutral processes and explicit pricing formulasare obtained.

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Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2006-28.

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Date of creation: 2006
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Handle: RePEc:crs:wpaper:2006-28
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