论文标题
从规避风险的随机优化分数功能的风险测量方法
A risk measurement approach from risk-averse stochastic optimization of score functions
论文作者
论文摘要
我们为规避风险的随机问题提出了风险测量方法。我们提供的结果可以保证我们的问题有解决方案。我们将Argmin的特性表征和探索为风险度量，最小值作为偏差度量。我们提供线性回归模型与我们的框架之间的联系。基于此概念，我们考虑有条件的风险，并提供最小偏差投资组合和线性回归之间的联系。此外，我们还将最佳复制对冲链接到我们的框架。
We propose a risk measurement approach for a risk-averse stochastic problem. We provide results that guarantee that our problem has a solution. We characterize and explore the properties of the argmin as a risk measure and the minimum as a deviation measure. We provide a connection between linear regression models and our framework. Based on this conception, we consider conditional risk and provide a connection between the minimum deviation portfolio and linear regression. Moreover, we also link the optimal replication hedging to our framework.
