基于多准则决策组合优化拓展Black-Litterman模型的应用

发布时间：2018-01-15 08:12

本文关键词：基于多准则决策组合优化拓展Black-Litterman模型的应用　出处：《复旦大学》2012年硕士论文　论文类型：学位论文

【摘要】：Markowitz在1952年提出基于均值-方差模型的投资组合理论,使资产配置跨入了数量化的时代,但是这个模型不能很好地融合投资者的主观观点。而1992年开始为人所知的Black-Litterman资产配置模型在半强式有效市场的假设基础上,结合使用了资本资产定价模型和贝叶斯规则,可以很方便地融入了投资者的主观观点。本文应用针对市场变量的Black-Litterman模型(BLm模型)来获得A股行业指数超额收益率的后验分布。现有的关于Black-Litterman模型的研究,常常在组合优化阶段采用Markowitz的临界线算法。这个优化方法假设所有投资者在市场均衡时使用同样的偏好,不能融合投资者个人偏好。为了处理个人偏好,本文使用了多准则决策(MCDM)方法进行组合优化。本文利用加权法把多目标优化问题转化为单目标优化问题。首先使用插值法把投资者的对各个目标的偏好转化为具体的效用函数,然后把各个效用函数整合成一个总体效用函数,作为优化问题的目标函数。在优化中采用遗传算法寻优,较好的解决了组合优化问题中的NP完全问题和函数搜索空间复杂(非凸、非凹)的问题。此外,本文验证了在MCDM的组合优化模型中,Black-Litterman模型的鲁棒性极其有限。提升资产配置的鲁棒性需要依靠添加约束条件,并采用适当的启发式算法来进行优化。
[Abstract]:In 1952, Markowitz put forward the portfolio theory based on mean-variance model, which made asset allocation enter the era of quantification. However, this model does not integrate investors' subjective views very well. And the Black-Litterman asset allocation model, known since 1992, is based on the assumption of semi-strong efficient markets. . A combination of capital asset pricing model and Bayesian rules is used. This paper applies the Black-Litterman model to market variables and blm model. To obtain the A-share industry index excess return posterior distribution. The existing research on Black-Litterman model. Markowitz's critical line algorithm is often used in portfolio optimization, which assumes that all investors use the same preference in market equilibrium. In order to deal with individual preferences, this paper uses the multi-criteria decision making (MCDM) method to optimize the portfolio. In this paper, the weighted method is used to transform the multi-objective optimization problem into a single-objective optimization problem. Firstly, the interpolation method is used to convert the investor's preference to each objective into a specific utility function. Then the utility function is integrated into a total utility function, which is the objective function of the optimization problem. Genetic algorithm is used to optimize the optimization. NP-complete problems in combinatorial optimization problems and complex (non-convex, non-concave) function search spaces are well solved. In addition, this paper verifies that the robustness of Black-Litterman model is very limited in MCDM's combinatorial optimization model. To improve the robustness of asset allocation, we need to add constraints. An appropriate heuristic algorithm is used for optimization.
【学位授予单位】：复旦大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F830.91;F224

【引证文献】