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1. Sparse Weighted Robust Support Vector Regression and Its Application in Index Tracking | |||
LIANG Rongmei | |||
Economics 18 September 2021 | |||
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Abstract:In recent years, support vector regression(SVR) is widely used in various fields because of its good generalization ability, especially in index tracking. Although many researchers have applied support vector and its sparse form to index tracking, The robustness and computational efficiency of the sparse model in high dimension have not been solved. Based on this consideration, this paper constructs a sparse adaptive support vector regression model which is robust in explanation variable and response variable directions. The model can be transformed into a constrained lad-lasso class problem, which is easy to calculation and analysis. At the same time, we show that under certain conditions, the model has a good ability to select variables and a very small bias. A large number of simulations support these theories, and this paper also makes an index tracking of Shanghai Stock Exchange 50. | |||
TO cite this article:LIANG Rongmei. Sparse Weighted Robust Support Vector Regression and Its Application in Index Tracking[OL].[18 September 2021] http://en.paper.edu.cn/en_releasepaper/content/4755388 |
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