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Enhanced index tracking with multiple time-scale analysis

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  • Li, Qian
  • Bao, Liang

Abstract

This paper considers the process optimal strategies for an enhanced index tracking problem. The investment goals are set to achieve a higher return than the benchmark by setting the portfolio's risk profile identical to the primary index risk factors. Return differences between the index and the tracking portfolio are classified as positive and negative series. Multiple time-scale features of each series are extracted by the method of empirical mode decomposition. Then the positive return deviations are modeled by trend-like low frequency behavior and the negative return deviations are modeled by a trendless high frequency behavior. By adopting an immunity-based multi-objective optimization algorithm, the solutions for the process optimal enhanced index tracking are developed. Five data sets drawn from major world markets are adopted to implement our approach. The computational results show the superiority of our model.

Suggested Citation

  • Li, Qian & Bao, Liang, 2014. "Enhanced index tracking with multiple time-scale analysis," Economic Modelling, Elsevier, vol. 39(C), pages 282-292.
    • Handle: RePEc:eee:ecmode:v:39:y:2014:i:c:p:282-292
    DOI: 10.1016/j.econmod.2014.03.009
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Doering, Jana & Kizys, Renatas & Juan, Angel A. & Fitó, Àngels & Polat, Onur, 2019. "Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends," Operations Research Perspectives, Elsevier, vol. 6(C).
    3. Li, Xuepeng & Xu, Fengmin & Jing, Kui, 2022. "Robust enhanced indexation with ESG: An empirical study in the Chinese Stock Market," Economic Modelling, Elsevier, vol. 107(C).
    4. Sant’Anna, Leonardo R. & Filomena, Tiago P. & Caldeira, João F., 2017. "Index tracking and enhanced indexing using cointegration and correlation with endogenous portfolio selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 146-157.
    5. Khan, Agha Salman M. & Verzijlbergh, Remco A. & Sakinci, Ozgur Can & De Vries, Laurens J., 2018. "How do demand response and electrical energy storage affect (the need for) a capacity market?," Applied Energy, Elsevier, vol. 214(C), pages 39-62.
    6. Sant’Anna, Leonardo Riegel & Caldeira, João Frois & Filomena, Tiago Pascoal, 2020. "Lasso-based index tracking and statistical arbitrage long-short strategies," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

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    More about this item

    Keywords

    Enhanced index tracking; Time-scale analysis; Tracking deviations decomposition; Multi-objective optimization;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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