Liability clearing in networks is modeled as global sections of a liability sheaf on directed hypergraphs, identified as a finite-limit construction with existence and uniqueness from lattice and metric theorems on payment objects.
arXiv preprint arXiv:1801.02091 , year=
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A large-deviations method generates plausible stress scenarios for financial losses by concentrating on most likely configurations conditional on large losses, recovering stressed loss laws even with sparse data.
citing papers explorer
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Clearing in Liability Networks via Sheaves on Directed Hypergraphs
Liability clearing in networks is modeled as global sections of a liability sheaf on directed hypergraphs, identified as a finite-limit construction with existence and uniqueness from lattice and metric theorems on payment objects.
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Generating Plausible Stress Scenarios via Large Deviations
A large-deviations method generates plausible stress scenarios for financial losses by concentrating on most likely configurations conditional on large losses, recovering stressed loss laws even with sparse data.