Abstract:CVaR is an important risk measurement for portfolio investment. It is of great significance to select robust asset portfolio in a CVaR based model so as to reduce both the time cost on management and economic expenditure. Some studies have shown that the decision of asset portfolio under CVaR criterion can be transformed to a classical quantile regression. Motivated by this transformation, an adaptive Lasso quantile regression with a network structure is developed to select high-dimensional assets. Adaptive Lasso can impose a weighted constraint on the regression coefficients, theoretically presenting selection consistency. Network structure, which is constructed based on the complicated network theory, can reflect the complex dynamic relations among all the assets. It can also benefit to the model's variable selection performance. Computation is achieved by a linear programming method, and a two-step iterative approach is proposed to deal with some special computational problems for the CVaR portfolio investment decision. Simulation study under various scenarios shows that the proposed model performs best in terms of variable selection and prediction, and the advantage of network structure becomes more and more obvious with an increasing correlation between variables. An empirical study with 249 stocks is conducted. With a rolling regression technique, the proposed model presents great robustness and applicable significance.
王小燕, 姚佳含, 袁欣. 带网络结构的自适应Lasso分位数回归及其应用[J]. 系统工程理论与实践, 2019, 39(8): 1954-1965.
WANG Xiaoyan, YAO Jiahan, YUAN Xin. Adaptive Lasso quantile regression with network structure and its application. Systems Engineering - Theory & Practice, 2019, 39(8): 1954-1965.
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