Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2607.10943

It Takes Two to Tango, but More to Assess Systemic Risk: Credit Networks Through the Lens of Hypergraphs

Federico D. Forte
View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper provides the first analysis of credit relationships between financial institutions and firms through the lens of hypergraphs. Unlike traditional network approaches, which rely on pairwise connections, this framework explicitly represents the shared exposure of multiple financial institutions to the same firm as a simultaneous multilateral relationship. The approach is applied empirically to Credit Registry data from the Central Bank of Argentina, covering the period from August 2023 to December 2025 and focusing on commercial loans between banks and firms. Traditional centrality metrics are compared with hypergraph-specific measures to identify systemically relevant institutions. The paper also proposes an adjusted version of H-eigenvector centrality that nonlinearly weights both the centrality of neighboring institutions and each creditor's lending amount, in order to assess the relevance of a bank within the network. The systemic impact of shocking the top-ranked institutions according to each centrality metric is then estimated through an adaptation of the DebtRank algorithm. The results show that the proposed framework identifies institutions with greater shock-amplification capacity, providing a complementary tool for financial supervision and regulation.

Explore related subjects

econ.EM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Federico D. Forte. 2026-07-12. It Takes Two to Tango, but More to Assess Systemic Risk: Credit Networks Through the Lens of Hypergraphs. https://arxiv.org/abs/2607.10943

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Testing for Monotone Equilibrium Strategies in Games of Incomplete Information

This paper develops a unified framework for testing monotonicity of Bayesian Nash equilibrium strategies in unobserved types in games of incomplete information. We show that, under symmetric independent private types, monotonicity of differentiable equilibrium strategies is equivalent to monotonicity of a quasi-inverse strategy identified from observed actions. This allows the problem to be reformulated as testing a countable set of moment inequalities involving unconditional expectations. We propose a Cramer-von Mises-type statistic with bootstrap critical values. The method accommodates covariates and game heterogeneity. Monte Carlo simulations demonstrate finite-sample performance, and an application to procurement auctions illustrates cartel detection.

econ.EM↗
arXiv2026

Ordinal Distributional Change and Conservative Transition Benchmarks: Measurement, Identification, and Inference

Repeated cross-sections reveal changes in ordinal distributions but not the transitions producing them. I axiomatically characterize a threshold-weighted probability metric for ordinal change from threshold-crossing principles. For any threshold-additive ordinal geometry, the discrepancy coincides with the Wasserstein--1 distance induced by that ground metric and measures minimum displacement; its optimizing plans define conservative transition benchmarks. With missing outcomes, I derive sharp identified sets for the discrepancy and endpoint-conditioned benchmark plans. I develop finite-sample-valid projection inference using randomized Monte Carlo calibration and global search with an almost-sure convergence guarantee. Applied to Arab Barometer data, the framework documents a robust shift toward broader and more regular remittance receipt in Lebanon. The discrepancy interval remains well separated from zero after allowing for item nonresponse and sampling uncertainty, while benchmark bounds provide strong numerical evidence that least-displacement restructuring excludes movement toward less frequent receipt and requires reassignment from nonreceipt to recurrent receipt.

econ.EM↗
arXiv2026

A Stochastic Nested Fixed Point Algorithm for Large-Scale BLP Estimation

We develop a stochastic nested fixed point (SNFP) estimator for random coefficients logit demand models that updates model parameters using stochastic gradients and performs demand inversion one market at a time. Relative to the conventional nested fixed point (NFP) estimator, SNFP substantially reduces memory requirements and computational cost, making estimation feasible in very large datasets. We establish the large-$T$ (number of markets) asymptotic properties of the estimator under regularity conditions. We also characterize the effect of sharing one block of simulation draws across markets and show how to correct for it. Monte Carlo simulations show that the SNFP estimator achieves statistical accuracy comparable to the NFP estimator, and in our benchmark a single online pass estimates a model with 100 million markets in about 5.5 hours. An empirical application using scanner data further demonstrates the practical advantages of SNFP for large-scale demand estimation.

econ.EM↗