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- W3008162807 abstract "Index tracking is a well-known passive management strategy that seeks to match the performance of a benchmark index. To the best of our knowledge, the existing literatures for sparse index tracking are mainly focus on the penalized least squares (LS) regression under the no-short selling constraint. In this paper, we propose an efficient sparse portfolio that based on composite quantile regression to simultaneously perform stock selection and capital allocation for high-dimensional index tracking. A special consideration is made concerning the budget constraint that has been ignored by the existing LS-type procedures. Furthermore, we develop a specialized linear programming algorithm for the implementation of the proposed method. Through the simulation, we show that the proposed method outperforms (or at least matches) existing procedures in terms of prediction accuracy and variable selection. Finally, we apply the proposed method to track the S&P 500 index in the New York Stock Exchange." @default.
- W3008162807 created "2020-03-06" @default.
- W3008162807 creator A5031810848 @default.
- W3008162807 date "2020-02-24" @default.
- W3008162807 modified "2023-10-18" @default.
- W3008162807 title "Efficient sparse portfolios based on composite quantile regression for high-dimensional index tracking" @default.
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- W3008162807 doi "https://doi.org/10.1080/00949655.2020.1731750" @default.
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