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An enhanced decision support approach for learning and tracking derivative index
Stockholm University, Faculty of Social Sciences, Stockholm Business School. Beihang University, China.
Stockholm University, Faculty of Social Sciences, Stockholm Business School. University of Chinese Academy of Sciences, China.
Number of Authors: 22019 (English)In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 88, p. 63-76Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 88, p. 63-76
Keywords [en]
Robust optimization, Risk management, Index tracking, Credit default swap (CDS)
National Category
Economics and Business
Identifiers
URN: urn:nbn:se:su:diva-172952DOI: 10.1016/j.omega.2018.10.021ISI: 000483657000006OAI: oai:DiVA.org:su-172952DiVA, id: diva2:1352297
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-18Bibliographically approved

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Wu, DexiangWu, Desheng Dash
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