Systemic Risk and the Dependence Structures

TL;DR

This paper introduces a dynamic Markov-based model to analyze dependence structures among financial institutions, providing measures for systemic risk that adapt over time and reflect changing economic conditions.

Contribution

It develops a flexible, Markov-structure-based framework for modeling dependence and systemic risk in financial systems, incorporating contagious credit rating jumps and evolving interdependence.

Findings

Different dependence structures lead to varying systemic instability measures.

Simulated Markov structures with the same chains can produce different systemic risk sequences.

The model captures contagion effects in credit migrations.

Abstract

We propose a dynamic model of dependence structure between financial institutions within a financial system and we construct measures for dependence and financial instability. Employing Markov structures of joint credit migrations, our model allows for contagious simultaneous jumps in credit ratings and provides flexibility in modeling dependence structures. Another key aspect is that the proposed measures consider the interdependence and reflect the changing economic landscape as financial institutions evolve over time. In the final part, we give several examples, where we study various dependence structures and investigate their systemic instability measures. In particular, we show that subject to the same pool of Markov chains, the simulated Markov structures with distinct dependence structures generate different sequences of systemic instability.