英文标题：
《Model risk in mean-variance portfolio selection: an analytic solution to
the worst-case approach》
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作者：
Roberto Baviera, Giulia Bianchi
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最新提交年份：
2019
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英文摘要：
In this paper we consider the worst-case model risk approach described in Glasserman and Xu (2014). Portfolio selection with model risk can be a challenging operational research problem. In particular, it presents an additional optimisation compared to the classical one. We find the analytical solution for the optimal mean-variance portfolio selection in the worst-case scenario approach. In the minimum-variance case, we prove that the analytical solution is significantly different from the one found numerically by Glasserman and Xu (2014) and that model risk reduces to an estimation risk. A detailed numerical example is provided.
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中文摘要：
在本文中，我们考虑Glasserman和Xu（2014）中描述的最坏情况模型风险方法。具有模型风险的投资组合选择可能是一个具有挑战性的运筹学问题。特别是，与经典优化相比，它提供了额外的优化。在最坏情况下，我们找到了最优均值-方差投资组合选择的解析解。在最小方差情况下，我们证明了解析解与Glasserman和Xu（2014）在数值上发现的解析解存在显著差异，并且模型风险降低为估计风险。给出了一个详细的数值例子。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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