Systemic risk measurement using network-based financial models

Abstract

The issue of systemic risk in financial systems has become a major issue of concern among the regulators, policymakers, and market participants, especially after events of financial turmoil. The complex interdependencies of financial institutions, firms and assets are not effectively considered by traditional risk assessment techniques, which limits their predictive ability. This paper explores measurement of systemic risk using financial model networks with focus on the contribution of interbank relationships, multiple layer interactions and market-based contagion effects. The study has a broad framework in measuring systemic risk by combining network topology metrics, simulation-based and market-based measures like CoVaR and MES. The empirical study indicates the relevance of centrality, the potential of contagion, and cross-layer interactions in identifying the systemically important institutions. The results highlight the usefulness of network-based methods in the context of early warning systems, stress testing, and policy formation, and provide recommendations on future studies in the field of multi-layer and AI-enhanced systemic risk modeling.