Robust Methods in Mathematical Finance

长期以来，随机模型已成功地应用于描述金融市场、做出投资决策、定价和对冲金融衍生品以及管理风险。 但这导致了对特定模型的过度依赖，并忽视了模型错误指定的可能性。 虽然在大多数工程应用中，稳健方法有着悠久的历史，但数学金融中的标准方法仍然是用单个概率度量来描述所有潜在的不确定性。 然而，在许多现实生活中，不可能确定未来事件的精确概率，如果现实并不完全像模型预测的那样展开，对特定假设敏感的财务决策规则可能导致灾难性的结果。 该项目的目标是开发考虑极端事件的财务决策方法，并在模型错误指定方面具有鲁棒性。 预计这些成果将带来稳健的投资、资产定价、对冲和风险管理方法。 研究生也参与了该项目的工作。该项目旨在创建可用于解决数学金融中相关问题的强大方法。 离散时间以及连续时间模型进行了研究。 该计划是建立非线性Riesz表示结果，可用于开发资产定价基本定理的稳健版本，以及相应的定价对偶公式，定价和对冲期权的稳健方法，以及处理模型不确定性下的最优资产配置问题的方法。 要解决的问题在于数学金融学，投资理论和金融经济学的交叉点。 方法从概率论，随机分析，泛函分析，决策论。

Stochastic models have long been successfully applied to describe financial markets, make investment decisions, price and hedge financial derivatives, and manage risks. But this has led to an over-reliance on particular models and a disregard of the possibility of model misspecification. While in most engineering applications robust methods have a long history, the standard approach in mathematical finance is still to describe all underlying uncertainty with a single probability measure. However, in many real-life situations it is not possible to determine the precise probabilities of future events, and financial decision rules that are sensitive to particular assumptions can lead to disastrous outcomes if reality does not exactly unfold as predicted by the model. The goal of this project is to develop methods of financial decision-making that take into account extreme events and are robust with respect to model misspecification. The outcomes are expected to lead to robust approaches to investing, asset pricing, hedging, and risk management. Graduate students are included in the work of the project. The project aims to create robust methods that can be used to address relevant problems in mathematical finance. Discrete-time as well as continuous-time models are studied. The plan is to establish non-linear Riesz representation results that can be used to develop robust versions of the fundamental theorem of asset pricing together with corresponding pricing duality formulas, robust methods for pricing and hedging options, as well as approaches to treat optimal asset allocation problems under model uncertainty. The problems to be addressed lie at the intersection of mathematical finance, investment theory, and financial economics. Methods from probability theory, stochastic analysis, functional analysis, and decision theory are used.