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Stressed portfolio optimization with semiparametric method.

Chuan-Hsiang Han1, Kun Wang2

  • 1Department of Quantitative Finance, National Tsing Hua University, Hsinchu, 30013 Taiwan.

Financial Innovation
|March 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible semiparametric method to manage tail risk in portfolio optimization, outperforming traditional approaches. It enhances investment strategies by accurately modeling extreme market events.

Keywords:
Copula methodKernel methodPortfolio optimizationRisk measureRisk-sensitive value measureScaling effectSemiparametric methodTail risk

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Area of Science:

  • Quantitative Finance
  • Financial Risk Management
  • Econometrics

Background:

  • Traditional mean-variance optimization struggles with unprecedented risks (tail risk).
  • Accurate modeling of extreme market events is crucial for portfolio performance.
  • Existing methods may not adequately capture complex dependencies in financial data.

Purpose of the Study:

  • To propose an innovative semiparametric method for stressed portfolio optimization.
  • To effectively address tail risk beyond Gaussian assumptions.
  • To determine optimal portfolio weights and investment scale for enhanced returns.

Main Methods:

  • Nonparametric estimation for marginal distributions.
  • Copula method for modeling the joint distribution of portfolio components.
  • Risk measure minimization and value measure maximization for optimization.

Main Results:

  • The semiparametric method provides flexible modeling for tail risk.
  • Optimal stressed portfolios show superior performance compared to mean-variance methods.
  • Empirical studies validate the statistical estimation and optimization techniques.

Conclusions:

  • The proposed semiparametric approach offers a robust solution for stressed portfolio optimization.
  • Model flexibility in accounting for tail risk is key to improving investment strategies.
  • This method enhances portfolio management by better handling extreme market conditions.