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Uncertain random enhanced index tracking for portfolio selection with parameter estimation and hypothesis test

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  • Li, Bo
  • Lu, Ziqiang

Abstract

The enhanced index tracking is an effective method for portfolio optimization that focuses on imitating the behavior of a special benchmark and achieving an excess return. In addition, uncertainty and randomness are two intrinsic attributes of real world systems. In this paper, we study an uncertain random enhanced index tracking for portfolio optimization with parameter estimation and hypothesis test based on chance theory, which combines uncertainty theory and probability theory. First, we formulate an uncertain random mean–variance enhanced index tracking model including both random risky securities and uncertain risky securities. Then the presented model is transformed into a quadratic programming problem and the analytical solutions are derived for some special cases. Furthermore, the uncertain hypothesis test is applied to examine the correctness of the unknown parameters solved by uncertain parameter estimation. Finally, three numerical simulations are presented for showing the applicability of the formulated models and the effectiveness of the enhanced index tracking strategies.

Suggested Citation

  • Li, Bo & Lu, Ziqiang, 2023. "Uncertain random enhanced index tracking for portfolio selection with parameter estimation and hypothesis test," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
  • Handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923000267
    DOI: 10.1016/j.chaos.2023.113125
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    References listed on IDEAS

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    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Tingqing Ye & Baoding Liu, 2022. "Uncertain hypothesis test with application to uncertain regression analysis," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 157-174, June.
    3. Bo Zhang & Jin Peng & Shengguo Li, 2015. "Uncertain programming models for portfolio selection with uncertain returns," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(14), pages 2510-2519, October.
    4. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari, 2021. "Trimmed fuzzy clustering of financial time series based on dynamic time warping," Annals of Operations Research, Springer, vol. 299(1), pages 1379-1395, April.
    5. Tanaka, Hideo & Guo, Peijun, 1999. "Portfolio selection based on upper and lower exponential possibility distributions," European Journal of Operational Research, Elsevier, vol. 114(1), pages 115-126, April.
    6. Liang-chuan Wu & I-chan Tsai, 2014. "Three fuzzy goal programming models for index portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(8), pages 1155-1169, August.
    7. Qin, Zhongfeng, 2015. "Mean-variance model for portfolio optimization problem in the simultaneous presence of random and uncertain returns," European Journal of Operational Research, Elsevier, vol. 245(2), pages 480-488.
    8. Rudolf, Markus & Wolter, Hans-Jurgen & Zimmermann, Heinz, 1999. "A linear model for tracking error minimization," Journal of Banking & Finance, Elsevier, vol. 23(1), pages 85-103, January.
    9. Li, Bo & Li, Xiangfa & Teo, Kok Lay & Zheng, Peiyao, 2022. "A new uncertain random portfolio optimization model for complex systems with downside risks and diversification," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
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