英文标题：
《Incorporating statistical model error into the calculation of
  acceptability prices of contingent claims》
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作者：
Martin Glanzer and Georg Ch. Pflug and Alois Pichler
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最新提交年份：
2019
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英文摘要：
  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 multi-stage 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.
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中文摘要：
确定未定权益的可接受价格需要为基础资产价格动态选择一个随机模型。在此模型下，通过随机优化可以找到最优的买卖价格。然而，基础资产价格过程的模型通常基于数据，并通过统计估计程序找到。我们通过基线模型的非参数邻域定义了可能估计模型的置信集。该邻域可作为模型不确定性下多阶段随机优化问题的模糊集。我们得到了可接受性定价问题的分布鲁棒解，并导出了对偶问题的表达式。此外，我们证明了嵌套距离的一般大偏差结果，这允许将模型模糊下的出价和要价与观测数据的质量联系起来。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Pricing of Securities        证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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