• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Global Optimization >Portfolio selection under model uncertainty: a penalized moment-based optimization approach
【24h】

Portfolio selection under model uncertainty: a penalized moment-based optimization approach

机译：模型不确定性下的投资组合选择：基于惩罚的基于矩的优化方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We present a new approach that enables investors to seek a reasonably robust policy for portfolio selection in the presence of rare but high-impact realization of moment uncertainty. In practice, portfolio managers face difficulty in seeking a balance between relying on their knowledge of a reference financial model and taking into account possible ambiguity of the model. Based on the concept of Distributionally Robust Optimization (DRO), we introduce a new penalty framework that provides investors flexibility to define prior reference models using the distributional information of the first two moments and accounts for model ambiguity in terms of extreme moment uncertainty. We show that in our approach a globally-optimal portfolio can in general be obtained in a computationally tractable manner. We also show that for a wide range of specifications our proposed model can be recast as semidefinite programs. Computational experiments show that our penalized moment-based approach outperforms classical DRO approaches in terms of both average and downside-risk performance using historical data.
机译：我们提出了一种新方法，使投资者能够在时刻但不确定的情况下实现罕见但影响很大的投资组合，以选择合理，稳健的投资组合政策。实际上，投资组合经理在依靠他们对参考财务模型的知识与考虑模型的可能歧义之间寻求平衡时会遇到困难。基于分布稳健优化（DRO）的概念，我们引入了新的惩罚框架，该框架为投资者提供了使用前两个时刻的分布信息来定义先前参考模型的灵活性，并考虑了极端时刻不确定性下的模型歧义。我们表明，在我们的方法中，总体上可以通过计算上容易处理的方式获得全局最优投资组合。我们还表明，对于各种各样的规范，我们提出的模型可以重铸为半定程序。计算实验表明，基于历史数据，基于惩罚性矩的惩罚方法在平均和下行风险表现方面均优于传统的DRO方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号