资产配置模型的选择：回报、风险抑或二者兼具  

A Choice of Asset Allocation Models: Return-based or Risk-based or Both

作　　者：谭华清[1,2] 赵学军[1] 黄一黎 Tan Huaqing;Zhao Xuejun;Huang Yili

机构地区：[1]嘉实基金管理有限公司 [2]北京大学光华管理学院 [3]嘉实财富管理有限公司

出　　处：《统计研究》2018年第7期62-76,共15页Statistical Research

摘　　要：近年来以风险平价为代表的基于风险的配置模型广为流行,这些模型的一大特点是放弃回报信息。而以均值方差模型代表的基于回报的配置模型则认为回报很重要而且默认对回报的预测是准确的。考虑到回报的可预测性得到了大量经验研究的支持,那么对于基于风险的配置模型而言,完全放弃回报则意味着有关回报的有用信息得不到充分利用。对于基于回报的配置模型而言,不考虑参数估计误差而且对输入参数敏感的缺点也大大抵消了它们利用回报信息带来的好处。那么,回报是否重要以及应该如何使用回报成了资产配置研究所面临的一个重大问题。为此,本文提出以风险平价为配置基准,以贝叶斯VAR回报预测为主观观点的Black-Litterman(贝叶斯BL)模型回答这一命题。利用1952—2016年的美国股票和债券季度数据,本文将贝叶斯BL模型与现有配置模型进行比较研究。实证结果表明,相比基于回报的配置模型,贝叶斯BL模型降低了组合风险;相比基于风险的配置模型,贝叶斯BL模型增强了组合回报。这些特性来自于它既能利用回报可预测性带来的有用信息,又能够发挥基于风险的配置模型在控制风险方面的优势。因此该模型表现出增强回报和控制风险兼具的特点,是具有潜力的资产配置新方案。In recent years,the risk-based asset allocation models becomes so popular,especially risk parity models. One of their most common features is that they no longer use the information on returns,while the return-based asset allocation models such as mean-variance model argue that the return is important assuming the forecasts on returns is accurate. Both of these treatments on returns are doubtful. As the asset return predictability has been proved by many empirical studies,in terms of risk-based asset allocation models,a complete dropping of the returns means the useful information on returns cannot be effectively used. For the return-based models,ignoring parameter estimation errors and insensitivity to input parameter would largely offset the benefits brought up by using the returns information. Therefore whether the returns are important here or not and how to use the returns information become a key issue in asset allocation. This paper proposes the Black-Litterman model（ Bayesian BL model） to answer such an important question,taking the risk parity as a benchmark for allocation and Bayesian VAR return forecasts as a subjective factor. By using the US stock and bond quarterly data from 1952 to 2016,a comparison is made between Bayesian BL model and currently available allocation models. The empirical findings show that,compared to the return-based models,theBayesian BL model reduces the portfolio risk; while compared to the risk-based models,the Bayesian BL model enhances the portfolio returns. These features stem from that it not only makes use of the useful information brought up by the returns predictability,but also takes the advantage of risk-based models in controlling the portfolio risk. In conclusion,the Bayesian BL model featured with enhanced returns and reduced risks is a new and potential scenario for asset allocation.

关 键 词：贝叶斯VAR 可预测性 均值方差 风险平价 Black-Litterman 

分 类 号：C812[社会学—统计学]