前沿 | 張金清：模糊厭惡下的動態最優投資策略(Journal of Banking & Finance)

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

本文在證券收益率可預測且證券交易需要成本的條件下，通過引入投資者對證券收益率及收益率預測因子的模糊厭惡，建立了動態投資組合優化模型🧙🏻‍♂️，並借助魯棒優化給出了最優的動態投資策略。進一步😮，本文詳細分析了最優投資策略的投資特征以及模糊厭惡對該策略的影響機製。研究發現，與Gârleanu和Pedersen（2013）的結論相比，模糊厭惡下的最優投資策略具備了一個新的特征🅾️，即目標證券投資組合偏重於收益率預測因子波動率更小🤙🏼、持有倉位更低或持倉成本更低的證券👗。由於預測因子波動率越小則預測偏差的發生概率越小，持有倉位越低或持倉成本越低則預測偏差的損失率越小，所以上述兩方面的改善將顯著降低收益率預測偏差可能帶來的投資損失🧏‍♀️。最後，本文將上述最優投資策略應用到商品期貨的動量投資中，發現與不考慮模糊厭惡的最優策略相比🧛‍♀️，上述策略能夠明顯降低收益率預測偏差引致的損失🐏，因而在樣本外能取得更好的投資業績。

關鍵詞🧑‍🎨📆：

模糊厭惡；投資組合優化🐇；魯棒優化