Abstract
We develop a theory of optimal stopping problems under ambiguity in continuous time. Using results from (backward) stochastic calculus, we characterize the value function as the smallest (nonlinear) supermartingale dominating the payoff process. For Markovian models, we derive an adjusted Hamilton–Jacobi–Bellman equation involving a nonlinear drift term that stems from the agent’s ambiguity aversion. We show how to use these general results for search problems and American options.
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Tese results have been presented at seminars in Freiburg (December 2008), CENOS Workshop, Münster (May 2009), ZiF Bielefeld (September 2009), IDEISCOR Conference Risk Sharing and Finance, Toulouse (September 2009), the Workshop on Risk Measures and Robust Optimization in Finance, NUS Singapore, November 2009, Oxford University (February 2010), and the Third Chinese-German Workshop on Stochastic Analysis and Related Fields, Beijing, 2010.
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Cheng, X., Riedel, F. Optimal stopping under ambiguity in continuous time. Math Finan Econ 7, 29–68 (2013). https://doi.org/10.1007/s11579-012-0081-6
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DOI: https://doi.org/10.1007/s11579-012-0081-6