摘要
金融数据的大维度性、高度正相关性及非正态性给投资组合中协方差矩阵的估计带来了巨大挑布,并借助l1惩罚项来获得大维逆协方差矩阵的稀疏估计。实证结果表明,相对于等权重模型、样本协方差模型及Glasso模型,Tlasso模型能显著提高大维协方差矩阵的估计效率,并选出最佳的投资组合。
The large dimensionality,highly positive correlation and non-normality of financial data bring great challenges to the estimation of covariance matrix in the portfolio.To solve this problem,the paper proposes a data-driven Tlasso portfolio model.The model assumes that the return on assets obeys multivariate t distribution,and obtains the sparse estimation of the large dimensional inverse covariance matrix by means of l1 penalized term.The empirical results show that compared with the equal-weight model,sample covariance model and Glasso model,the Tlasso model significantly improves the estimation efficiency of large dimensional covariance matrix,and can select the best portfolio.
作者
袁欣
俞卫琴
Yuan Xin;Yu Weiqin(School of Mathematics and Statistics,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《统计与决策》
CSSCI
北大核心
2021年第6期60-63,共4页
Statistics & Decision
基金
国家自然科学基金资助项目(11602134,11772148)
全国统计科学研究一般项目(2018LY16)