Pith. sign in

REVIEW 1 cited by

Quasi-Logconvex Measures of Risk

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2208.07694 v1 pith:LJHUMOCN submitted 2022-07-25 q-fin.RM math.PR

Roger J. A. Laeven , Emanuela Rosazza Gianin This is my paper · ORCID
classification q-fin.RMmath.PR
keywords measuresriskquasi-logconvexclassgeneralizequasi-convexrepresentationacceptance
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

This paper introduces and fully characterizes the novel class of quasi-logconvex measures of risk, to stand on equal footing with the rich class of quasi-convex measures of risk. Quasi-logconvex risk measures naturally generalize logconvex return risk measures, just like quasi-convex risk measures generalize convex monetary risk measures. We establish their dual representation and analyze their taxonomy in a few (sub)classification results. Furthermore, we characterize quasi-logconvex risk measures in terms of properties of families of acceptance sets and provide their law-invariant representation. Examples and applications to portfolio choice and capital allocation are also discussed.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generalized Orlicz premia

    q-fin.RM 2025-07 conditional novelty 7.0 of 10

    Generalized Orlicz premia with non-convex loss functions unify quantiles, expectiles, and L^p-quantiles, and cash-additivity characterizes them as L^p-quantiles.

Pith tools