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Stressed portfolio optimization with semiparametric method

Chuan-Hsiang Han () and Kun Wang ()
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Chuan-Hsiang Han: National Tsing Hua University
Kun Wang: National Tsing Hua University

Financial Innovation, 2022, vol. 8, issue 1, 1-34

Abstract: 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.

Keywords: Portfolio optimization; Tail risk; Semiparametric method; Kernel method; Copula method; Risk measure; Risk-sensitive value measure; Scaling effect (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00333-w

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DOI: 10.1186/s40854-022-00333-w

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