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An enhanced decision support approach for learning and tracking derivative index

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  • Wu, Dexiang
  • Dash Wu, Desheng

Abstract

Tracking the movement of an index involves the parameter learning from data and algorithm design for solving the decision model. In this paper, we present a factor induced robust index tracking model to protect against the parameter estimation error and immunize both systematic and default risks of tracking portfolios. A Lagrangian-based algorithm is applied to approximate optimal solutions and enhance the capacity of the decision model. Two types of inequalities are derived to strengthen the Lagrangian lower bound and speed up the whole Lagrangian Relaxation (LR) method. With the designed system, we investigate large Credit Default Swap (CDS) dataset that includes 1246 daily observations across near 500 individual contracts. We show that the fluctuation range of portfolio out-of-sample returns can be shrunk significantly by using the proposed robust counterpart, e.g. from [−12%, 12%] to [−4%, 4%] in the second half of 2013, and other comparison metrics such as Sharpe ratio and tracking error to transaction costs (TE/TC) ratio could also be consistently improved.

Suggested Citation

  • Wu, Dexiang & Dash Wu, Desheng, 2019. "An enhanced decision support approach for learning and tracking derivative index," Omega, Elsevier, vol. 88(C), pages 63-76.
    • Handle: RePEc:eee:jomega:v:88:y:2019:i:c:p:63-76
    DOI: 10.1016/j.omega.2018.10.021
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    1. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.

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