基于回归模型的股票指数追踪问题实证研究

An Empirical Study of the Stock Index Tracking Problem Based on a Regression Model

随着股票价格指数的发展与演变,股指对于投资的作用显得尤为重要。本文采用最小二乘估计、岭估计、绝对约束回归(Lasso)、弹性约束估计及两步估计对深证区块链50指数进行指数追踪,得到相应的投资组合,并将Cp准则和CV准则下的Lasso和岭估计作对比,得出结论:Cp准则下岭估计更好,CV准则下Lasso更好,两步估计下Lasso进行变量选择后用刘估计进行回归效果较好。

With the development and evolution of stock price index, the role of stock index in investment is particularly important. This paper uses least squares estimation, ridge estimation, absolute constraint regression (Lasso), elastic constraint estimation and two-step estimation to track the SZSE Blockchain 50 Index to obtain the corresponding investment portfolio. The Lasso and ridge estimation are compared, and it is concluded that the ridge estimation is better under the Cp criterion, the Lasso under the CV criterion is better, and the Lasso performs variable selection under the two-step estimation.