Reduced-form framework for multiple ordered default times under model uncertainty

TL;DR

This paper develops a reduced-form framework for modeling multiple ordered default times under model uncertainty, introducing a sublinear conditional operator to handle nondominated probability measures and applying it to credit derivative valuation.

Contribution

It generalizes existing single-default models to multiple defaults and incorporates model uncertainty using a novel sublinear operator.

Findings

Introduces a sublinear conditional operator for multiple default times

Extends reduced-form models to account for model uncertainty

Applies framework to credit portfolio derivative valuation

Abstract

In this paper we introduce a sublinear conditional operator with respect to a family of possibly nondominated probability measures in presence of multiple ordered default times. In this way we generalize the results of [5], where a reduced-form framework under model uncertainty for a single default time is developed. Moreover, we use this operator for the valuation of credit portfolio derivatives under model uncertainty.