英文标题：
《A General Duality Relation with Applications in Quantitative Risk
Management》
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作者：
Raphael Hauser, Sergey Shahverdyan and Paul Embrechts
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最新提交年份：
2014
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英文摘要：
A fundamental problem in risk management is the robust aggregation of different sources of risk in a situation where little or no data are available to infer information about their dependencies. A popular approach to solving this problem is to formulate an optimization problem under which one maximizes a risk measure over all multivariate distributions that are consistent with the available data. In several special cases of such models, there exist dual problems that are easier to solve or approximate, yielding robust bounds on the aggregated risk. In this chapter we formulate a general optimization problem, which can be seen as a doubly infinite linear programming problem, and we show that the associated dual generalizes several well known special cases and extends to new risk management models we propose.
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中文摘要：
风险管理中的一个基本问题是，在几乎没有或根本没有数据可用于推断其依赖性信息的情况下，不同风险源的稳健聚合。解决这个问题的一种流行方法是制定一个优化问题，在该问题下，在所有与可用数据一致的多元分布上，最大化风险度量。在这类模型的几种特殊情况下，存在更容易解决或近似的对偶问题，从而对聚合风险产生鲁棒界。在这一章中，我们提出了一个一般的优化问题，可以看作是一个双无限线性规划问题，并且我们证明了相关的对偶推广了几个著名的特殊情况，并扩展到我们提出的新的风险管理模型。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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