基于熵补偿的Black-Litterman模型的投资组合

doi:10.3969/j.issn.0253-2778.2015.12.011

中国科学技术大学学报 ›› 2015, Vol. 45 ›› Issue (12): 1030-1035.DOI: 10.3969/j.issn.0253-2778.2015.12.011

• 研究论文:管理科学与工程 • 上一篇下一篇

基于熵补偿的Black-Litterman模型的投资组合

朱业春，曹崇延

中国科学技术大学管理学院，安徽合肥 230026

收稿日期:2014-12-22 修回日期:2015-07-14 接受日期:2015-07-14 出版日期:2023-03-27 发布日期:2015-07-14
通讯作者: 曹崇延
作者简介:朱业春，男，1990年生，硕士. 研究方向：金融工程. E-mail: ycz@mail.ustc.edu.cn

Portfolio based on Black-Litterman model with entropy compensation

ZHU Yechun, CAO Chongyan

School of Management, University of Science and Technology of China, Hefei 230026, China

Received:2014-12-22 Revised:2015-07-14 Accepted:2015-07-14 Online:2023-03-27 Published:2015-07-14

摘要/Abstract

摘要： Black-Litterman模型在传统的投资组合模型中加入了投资者的主观判断，在金融市场上获得了一定的认可.在该模型基础上，提出了一种基于熵补偿的最优化方法.首先根据历史数据，利用AR-TGARCH模型预测收益率和波动率，作为模型的输入变量，代替了纯粹意义上分析师的主观决定；其次加入信息熵，改进了传统最优化效用函数，通过求解非线性规划问题得到资产的最优组合权重.实证研究表明，该模型较之其他投资组合模型，能够获得更高的收益，具有更强的应用性.

关键词: 熵补偿, Black-Litterman模型, AR-TGARCH, 投资组合

Abstract: The Black-Litterman model can be recognized in the financial markets in combination with subjective judgment of the investors and the traditional portfolio. Based on the model, the optimization method with entropy compensation was presented. AR-TGARCH was used on the basis of historical data to predict yields and volatility, as the input variable, instead of the pure sense of the analysts subjective decision. The optimal combination of asset weights were obtained by solving the nonlinear programming problem with entropy compensation. Empirical study shows that the new model achieves better return and has stronger applicability than other portfolio models.

Key words: entropy compensation, Black-Litterman model, AR-TGARCH, portfolio

中图分类号:

F830.91

朱业春, 曹崇延. 基于熵补偿的Black-Litterman模型的投资组合[J]. 中国科学技术大学学报, 2015, 45(12): 1030-1035.

ZHU Yechun, CAO Chongyan. Portfolio based on Black-Litterman model with entropy compensation[J]. Journal of University of Science and Technology of China, 2015, 45(12): 1030-1035.

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基于熵补偿的Black-Litterman模型的投资组合

Portfolio based on Black-Litterman model with entropy compensation

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