摘要
增强指数模型旨在用少量的成份股构建指数跟踪组合,以期在跟踪指数趋势的同时获取高于指数平均收益的超额收益.当市场指数下跳时,跟踪组合会跟随指数趋势而产生巨额损失,因此有必要在传统的增强指数模型中加入下端风险约束,以阻止跟踪组合随指数下跳的风险.下偏矩(lower partial moment,LPM)作为下端风险度量工具,具有良好的理论性质且涵盖了损失概率、期望损失、下半方差等经典度量方法,因此本文构建LPM约束下的增强指数模型.该模型有三个特点:第一,具有更加一般的目标函数且允许投资者根据自身的目标设置参数,通过调节模型中的权衡参数,模型可以退化到传统的指数复制模型和超额收益最大化模型;第二,加入非参数LPM约束,以控制跟踪组合的下端风险;第三,得到目标函数和非参数1-阶LPM的凸性,证明了基于非参数1-阶LPM约束的增强指数模型是凸优化问题.模拟和实证结果表明,本文的模型能够控制下端风险并获得超额收益.
Enhanced indexation model(EIM)adopts some of the constituents to construct a portfolio to track the benchmark index and obtain excess returns.As the benchmark index falls,the tracking portfolio follows and yield negative returns.Therefore,it is necessary to add downside risk constraints to the traditional EIM in order to prevent the tracking portfolio from jumping in conjunction with severe market recession.It is noticeable that,as a downside risk measure,the lower partial moment(LPM)has good theoretical properties and covers the classical measures such as loss probability,expected loss,and lower semi・variance.In this paper,an EIM w让h the LPM constraint is constructed・Its key characteristics are threefblds.First,our model has a more general objective function to meet investors*risk preferences.By adjusting the balanced parameter,our model can be degenerated to the traditional index replication model and excess returns maximum model.Second,the nonparametric LPM constraint is added to our model in order to control the downside risk of the tracking portfolio.Third,the objective function and the nonparametric first-order LPM are proven to be a convex function of the portfolios position,and the EIM with nonparametric first-order LPM are proven to be a convex optimization problem.Finally,Monte Carlo simulation and the empirical results show that our model can effec・tively control the downside risk and obtain excess returns.
作者
黄金波
李仲飞
邹新月
HUANG Jin-bo;LI Zhong-fei;ZOU Xin-yue(School of Finance,Guangdong University of Finance&Economics,Guangzhou 510320,China;Sun Yat-sen Business School,Sun Yat-sen University,Guangzhou 510275,China)
出处
《管理科学学报》
CSSCI
CSCD
北大核心
2019年第12期56-69,共14页
Journal of Management Sciences in China
基金
国家自然科学基金资助项目(71721001,1603058,71971068)
教育部人文社会科学研究项目(16YJC790033)
广东省自然科学基金资助项目(2016A030313656,2014A030310428,2014A030310305,2014A030312003)
广东省哲学社会科学规划项目(GD15YYJ06,GD15XYJ03).
关键词
增强指数
下偏矩
非参数估计
凸优化
enhanced indexation
lower partial moment
nonparametric estimation
convex optimization
