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Pricing and Inference with Mixtures of Conditionally Normal Processes

  • Bertholon, H.
  • Monfort, A.
  • Pegoraro, F.

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

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Paper provided by Banque de France in its series Working papers with number 188.

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Length: 55 pages
Date of creation: 2007
Date of revision:
Handle: RePEc:bfr:banfra:188
Contact details of provider: Postal: Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS
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