Dynamic Systemic Risk: Networks in Data Science

Abstract

In this article, the authors propose a theory-driven framework for monitoring system-wide risk by extending data science methods widely deployed in social networks. Their approach extends the one-firm Merton credit risk model to a generalized stochastic network-based framework across all financial institutions, comprising a novel approach to measuring systemic risk over time. The authors identify four desired properties for any systemic risk measure. They also develop measures for the risks created by each individual institution and a measure for risk created by each pairwise connection between institutions. Four specific implementation models are then explored, and brief empirical examples illustrate the ease of implementation of these four models and show general consistency among their results. TOPICS:Big data/machine learning, financial crises and financial market history, other

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 19 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.