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Stressed portfolio optimization with semiparametric method

Author

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  • Chuan-Hsiang Han

    (National Tsing Hua University)

  • Kun Wang

    (National Tsing Hua University)

Abstract

Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks, while the traditional mean–variance approach may fail to perform well. This study proposes an innovative semiparametric method consisting of two modeling components: the nonparametric estimation and copula method for each marginal distribution of the portfolio and their joint distribution, respectively. We then focus on the optimal weights of the stressed portfolio and its optimal scale beyond the Gaussian restriction. Empirical studies include statistical estimation for the semiparametric method, risk measure minimization for optimal weights, and value measure maximization for the optimal scale to enlarge the investment. From the outputs of short-term and long-term data analysis, optimal stressed portfolios demonstrate the advantages of model flexibility to account for tail risk over the traditional mean–variance method.

Suggested Citation

  • Chuan-Hsiang Han & Kun Wang, 2022. "Stressed portfolio optimization with semiparametric method," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-34, December.
    • Handle: RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00333-w
    DOI: 10.1186/s40854-022-00333-w
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    References listed on IDEAS

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    Cited by:

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    2. 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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