Journal of Banking & Finance (June 2017) ， Jinqing Zhang,Zeyu Jin, Yunbi An 发表文章 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

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