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前沿 | 张金清:模糊厌恶下的动态最优投资策略(Journal of Banking & Finance)

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Journal of Banking & Finance (June 2017) , Jinqing Zhang,Zeyu Jin, Yunbi An 发表文章 Dynamic portfolio optimization with ambiguity aversion.



张金清
作者简介:
张金清,复旦大学金融学教授、博士生导师。现任复旦大学应用经济学博士后流动站站长(经院)、复旦大学经济学院副院长、金融研究院常务副院长、教育部金融创新研究生开放实验室主任、经济学院学位委员会副主席、经济学院教学指导委员会主席、全国金融专业学位研究生教育指导委员会委员。主要研究领域:金融风险管理、数理金融、金融工程、金融开放与金融安全、行为金融、经济金融中的非线性问题分析等



Dynamic portfolio optimization with ambiguity aversion

JinqingZhang

Instituteof Financial Studies, Fudan University

ZeyuJin

Instituteof Financial Studies, Fudan University

YunbiAn

Odette School of Business, University of Windsor


内容简介

Abstract:

This paper investigates portfolio selection in the presence of transaction costs and ambiguity about return predictability. By distinguishing between ambiguity aversion to returns and to return predictors, we derive the optimal dynamic trading rule in closed form within the framework of Gârleanu and Pedersen(2013), using the robust optimization method. We characterize its properties and the unique mechanism through which ambiguity aversion impacts the optima lrobust strategy. In addition to the two trading principles documented in Gârleanu and Pedersen (2013), our model further implies that the robust strategy aims to reduce the expected loss arising from estimation errors.Ambiguity-averse investors trade toward an aim portfolio that gives less weight to highly volatile return-predicting factors, and loads less on the securities that have large and costly positions in the existing portfolio. Using data on various commodity futures, we show that the robust strategy outperforms the corresponding non-robust strategy in out-of-sample tests.


Keywords:

Ambiguity aversion; Portfolio optimization;Robust optimization


摘要:

本文在证券收益率可预测且证券交易需要成本的条件下,通过引入投资者对证券收益率及收益率预测因子的模糊厌恶,建立了动态投资组合优化模型,并借助鲁棒优化给出了最优的动态投资策略。进一步,本文详细分析了最优投资策略的投资特征以及模糊厌恶对该策略的影响机制。研究发现,与Gârleanu和Pedersen(2013)的结论相比,模糊厌恶下的最优投资策略具备了一个新的特征,即目标证券投资组合偏重于收益率预测因子波动率更小、持有仓位更低或持仓成本更低的证券。由于预测因子波动率越小则预测偏差的发生概率越小,持有仓位越低或持仓成本越低则预测偏差的损失率越小,所以上述两方面的改善将显著降低收益率预测偏差可能带来的投资损失。最后,本文将上述最优投资策略应用到商品期货的动量投资中,发现与不考虑模糊厌恶的最优策略相比,上述策略能够明显降低收益率预测偏差引致的损失,因而在样本外能取得更好的投资业绩。


关键词:

模糊厌恶;投资组合优化;鲁棒优化


 
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