Computing best bounds for nonlinear risk measures with partial information

Man Hong Wong, Shuzhong Zhang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Extreme events occur rarely, but these are often the circumstances where an insurance coverage is demanded. Given the first, say, n moments of the risk(s) of the events, one is able to compute or approximate the tight bounds for risk measures in the form of E(ψ(x)) through semidefinite programmings (SDP), via distributional robust optimization formulations. Existing results in the literature have already demonstrated the power of this technique when ψ (x) is linear or piecewise linear. In this paper, we extend the technique in the case where ψ (x) is a polynomial or fractional polynomial.

Original languageEnglish (US)
Pages (from-to)204-212
Number of pages9
JournalInsurance: Mathematics and Economics
Issue number2
StatePublished - Mar 1 2013


  • Moment bounds
  • Nonlinear risk
  • Risk management
  • Robust optimization
  • Semidefinite programming (SDP)
  • Worst-case scenario

Fingerprint Dive into the research topics of 'Computing best bounds for nonlinear risk measures with partial information'. Together they form a unique fingerprint.

Cite this