Abstract
In this paper we consider the notion of dynamic risk measures, which we will motivate as a reasonable tool in risk management. It is possible to reformulate an example of such a risk measure in terms of the value functions of a Markov decision model (MDM). Based on this observation the model is generalized to a setting with incomplete information about the risk distribution which can be seen as model uncertainty. This issue can be incorporated in the dynamic risk measure by extending the MDM to a Bayesian decision model. Moreover, it is possible to discuss the effect of model uncertainty on the risk measure in binomial models. All investigations are illustrated by a simple but useful coin tossing game proposed by Artzner and by the classic Cox–Ross–Rubinstein model.
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