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Index tracking model, downside risk and non-parametric kernel estimation

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  • Huang, Jinbo
  • Li, Yong
  • Yao, Haixiang

Abstract

In this paper, we propose an index tracking model with the conditional value-at-risk (CVaR) constraint based on a non-parametric kernel (NPK) estimation framework. In theory, we demonstrate that the index tracking model with the CVaR constraint is a convex optimization problem. We then derive NPK estimators for tracking errors and CVaR, and thereby construct the NPK index tracking model. Monte Carlo simulations show that the NPK method outperforms the linear programming (LP) method in terms of estimation accuracy. In addition, the NPK method can enhance computational efficiency when the sample size is large. Empirical tests show that the NPK method can effectively control downside risk and obtain higher excess returns, in both bearish and bullish market environments.

Suggested Citation

  • Huang, Jinbo & Li, Yong & Yao, Haixiang, 2018. "Index tracking model, downside risk and non-parametric kernel estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 103-128.
    • Handle: RePEc:eee:dyncon:v:92:y:2018:i:c:p:103-128
    DOI: 10.1016/j.jedc.2018.04.008
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    Cited by:

    1. Yao, Haixiang & Huang, Jinbo & Li, Yong & Humphrey, Jacquelyn E., 2021. "A general approach to smooth and convex portfolio optimization using lower partial moments," Journal of Banking & Finance, Elsevier, vol. 129(C).
    2. Huang, Jinbo & Ding, Ashley & Li, Yong & Lu, Dong, 2020. "Increasing the risk management effectiveness from higher accuracy: A novel non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    3. Huang, Jinbo & Li, Yong & Yao, Haixiang, 2022. "Partial moments and indexation investment strategies," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 39-59.
    4. Zhifeng Dai & Jie Kang, 2022. "Some new efficient mean–variance portfolio selection models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4784-4796, October.
    5. Li, Helong & Huang, Qin & Wu, Baiyi, 2021. "Improving the naive diversification: An enhanced indexation approach," Finance Research Letters, Elsevier, vol. 39(C).
    6. 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.
    7. Sant’Anna, Leonardo Riegel & Righi, Marcelo Brutti & Müller, Fernanda Maria & Guedes, Pablo Cristini, 2022. "Risk measure index tracking model," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 361-383.

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    More about this item

    Keywords

    Non-parametric kernel estimation; Index tracking model; Conditional value-at-risk;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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