Set risk measures

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Righi, Marcelo, Horta, Eduardo, Moresco, Marlon
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866913145010782208
author Righi, Marcelo
Horta, Eduardo
Moresco, Marlon
author_facet Righi, Marcelo
Horta, Eduardo
Moresco, Marlon
contents We introduce set risk measures (SRMs), real-valued maps defined on the family of non-empty closed bounded sets of essentially bounded random variables. SRMs extend traditional scalar risk measures by assigning a single capital requirement to an entire set of positions. We develop an axiomatic framework for SRMs, adapting classical properties such as monotonicity, translation invariance, convexity, and positive homogeneity to set arithmetic. The main technical contribution is a dual representation of convex SRMs through the \strict{} topology and regular $τ$-additive unit-mass measures. We also characterize worst-case SRMs and present examples related to systemic risk, Knightian uncertainty, and preference representations.
format Preprint
id arxiv_https___arxiv_org_abs_2407_18687
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Set risk measures
Righi, Marcelo
Horta, Eduardo
Moresco, Marlon
Mathematical Finance
Risk Management
We introduce set risk measures (SRMs), real-valued maps defined on the family of non-empty closed bounded sets of essentially bounded random variables. SRMs extend traditional scalar risk measures by assigning a single capital requirement to an entire set of positions. We develop an axiomatic framework for SRMs, adapting classical properties such as monotonicity, translation invariance, convexity, and positive homogeneity to set arithmetic. The main technical contribution is a dual representation of convex SRMs through the \strict{} topology and regular $τ$-additive unit-mass measures. We also characterize worst-case SRMs and present examples related to systemic risk, Knightian uncertainty, and preference representations.
title Set risk measures
topic Mathematical Finance
Risk Management
url https://arxiv.org/abs/2407.18687