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Peer-reviewedCC BY 4.0Mathematics

Downside Risk Optimization Under Asymmetric Laplace Distribution

  • Zheng Liu1
  • Wenhao Qian2
  • Jingyi Wu3
  • Yang Yang1
  1. 1Center for Financial Engineering, Soochow University, Suzhou 215006, China
  2. 2School of Digital Economics and Management, Suzhou City University, Suzhou 215104, China
  3. 3School of Mathematical Sciences, Soochow University, Suzhou 215006, China
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Abstract

In this paper, we develop a downside risk portfolio optimization model based on the Asymmetric Laplace (AL) distribution, where downside risk is measured by the Lower Partial Moment (LPM). Under this framework, we explicitly derive the expressions for the first- and second-order LPMs and develop a semi-analytical framework to determine the optimal portfolio weights. Empirical analysis compares the proposed AL distribution-based strategy with the normal-based downside risk minimization and equally weighted portfolio. The results show that the AL distribution-based strategy achieves better downside risk control and improved risk-adjusted performance compared to normal-based benchmarks.

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Cite

BibTeXRIS
BibTeX
@article{liu2026downside,
  title = {{Downside Risk Optimization Under Asymmetric Laplace Distribution}},
  author = {Zheng Liu and Wenhao Qian and Jingyi Wu and Yang Yang},
  journal = {Mathematics},
  year = {2026},
  month = oct,
  doi = {10.3390/math14213923},
  url = {https://www.mdpi.com/2227-7390/14/21/3923},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Downside Risk Optimization Under Asymmetric Laplace Distribution
AU  - Zheng Liu
AU  - Wenhao Qian
AU  - Jingyi Wu
AU  - Yang Yang
PY  - 2026
DA  - 2026/10/27
JO  - Mathematics
DO  - 10.3390/math14213923
UR  - https://www.mdpi.com/2227-7390/14/21/3923
AB  - In this paper, we develop a downside risk portfolio optimization model based on the Asymmetric Laplace (AL) distribution, where downside risk is measured by the Lower Partial Moment (LPM). Under this framework, we explicitly derive the expressions for the first- and second-order LPMs and develop a semi-analytical framework to determine the optimal portfolio weights. Empirical analysis compares the proposed AL distribution-based strategy with the normal-based downside risk minimization and equally weighted portfolio. The results show that the AL distribution-based strategy achieves better downside risk control and improved risk-adjusted performance compared to normal-based benchmarks.
SN  - 2227-7390
ER  -