Collateral Unchained: Rehypothecation networks, concentration and systemic effects

TL;DR

This paper models how network structures of rehypothecation influence collateral volume, velocity, and systemic risk, revealing trade-offs between liquidity and vulnerability to cascades in financial networks.

Contribution

It introduces a simple model linking network topology to collateral dynamics and systemic risk, highlighting the impact of concentration and cyclic structures.

Findings

Collateral volume and velocity depend on network features like chain length and cyclicity.

Core-periphery networks enable large collateral increases with low density.

Highly concentrated collateral flows increase systemic risk through cascades.

Abstract

We study how network structure affects the dynamics of collateral in presence of rehypothecation. We build a simple model wherein banks interact via chains of repo contracts and use their proprietary collateral or re-use the collateral obtained by other banks via reverse repos. In this framework, we show that total collateral volume and its velocity are affected by characteristics of the network like the length of rehypothecation chains, the presence or not of chains having a cyclic structure, the direction of collateral flows, the density of the network. In addition, we show that structures where collateral flows are concentrated among few nodes (like in core-periphery networks) allow large increases in collateral volumes already with small network density. Furthermore, we introduce in the model collateral hoarding rates determined according to a Value-at-Risk (VaR) criterion, and we then study the emergence of collateral hoarding cascades in different networks. Our results highlight that network structures with highly concentrated collateral flows are also more exposed to large collateral hoarding cascades following local shocks. These networks are therefore characterized by a trade-off between liquidity and systemic risk.