Titel
Incorporating statistical model error into the calculation of acceptability prices of contingent claims
Autor*in
Alois Pichler
Faculty of Mathematics, Chemnitz University of Technology
Abstract
The determination of acceptability prices of contingent claims requires the choice of a stochastic model for the underlying asset price dynamics. Given this model, optimal bid and ask prices can be found by stochastic optimization. However, the model for the underlying asset price process is typically based on data and found by a statistical estimation procedure. We define a confidence set of possible estimated models by a nonparametric neighborhood of a baseline model. This neighborhood serves as ambiguity set for a multistage stochastic optimization problem under model uncertainty. We obtain distributionally robust solutions of the acceptability pricing problem and derive the dual problem formulation. Moreover, we prove a general large deviations result for the nested distance, which allows to relate the bid and ask prices under model ambiguity to the quality of the observed data.
Stichwort
Multistage stochastic optimizationDistributionally robust optimizationModel ambiguityConfidence regionsNested distanceWasserstein distanceAcceptability pricingBid–ask spread
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:937303
Erschienen in
Titel
Mathematical Programming
Verlag
Springer Nature
Erscheinungsdatum
2018
Zugänglichkeit
Rechteangabe
© The Author(s) 2018

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