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Pricing and Inference with Mixtures of Conditionally Normal Processes

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  • Henri Bertholon

    (Crest)

  • Alain Monfort

    (Crest)

  • Fulvio Pegoraro

    (Crest)

Abstract
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.

Suggested Citation

  • Henri Bertholon & Alain Monfort & Fulvio Pegoraro, 2006. "Pricing and Inference with Mixtures of Conditionally Normal Processes," Working Papers 2006-28, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2006-28
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    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
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