ABSTRACT
This paper developed a factor-based robust approach to improve the tracking fund's stability. Similar to the financial crisis, the recent coronavirus pandemic amplify the global market volatility significantly, which suggests that healthcare-based factor can be used to hedge against the jump risk. The index tracking fund is constructed by a developed cardinality constrained conic programming. To overcome the large-scale computational challenge, we decompose the problem into two simplified cases and quickly calculate the tighter lower bound and its feasible upper bound. In addition, a subgradient-based inequalities are derived to exclude the suboptimal points that have been traveled in previous iterations. It turns out that the proposed model, along with the designed solving technique, can be used as an alternative to build reliable tracking portfolios. We demonstrate the effectiveness and robustness of the proposed method by testing different large real data sets. © 2022 Elsevier Ltd
ABSTRACT
This paper developed a factor-based robust approach to improve the tracking fund’s stability. Similar to the financial crisis, the recent coronavirus pandemic amplify the global market volatility significantly, which suggests that healthcare-based factor can be used to hedge against the jump risk. The index tracking fund is constructed by a developed cardinality constrained conic programming. To overcome the large-scale computational challenge, we decompose the problem into two simplified cases and quickly calculate the tighter lower bound and its feasible upper bound. In addition, a subgradient-based inequalities are derived to exclude the suboptimal points that have been traveled in previous iterations. It turns out that the proposed model, along with the designed solving technique, can be used as an alternative to build reliable tracking portfolios. We demonstrate the effectiveness and robustness of the proposed method by testing different large real data sets.