Fire sales and systemic risk in financial networks

Abstract

This thesis studies systemic risk through direct and indirect contagion in financial networks.

The first and main part is devoted to modelling the phenomenon of fire sales in a network of financial institutions with common asset holdings, subject to leverage or capital constraints. In contrast to balance sheet contagion based on direct linkages, this price-mediated contagion occurs through common asset holdings, which we quantify through liquidity-weighted overlaps across portfolios. Exposure to price-mediated contagion leads to the concept of indirect exposure to an asset class, as a consequence of which the risk of a portfolio depends on the network of overlapping portfolios.

Our model provides an operational systemic stress testing framework for quantifying the exposure of the financial system to these effects. Using data from the European Banking Authority, we examine the exposure of the EU banking system to price-mediated contagion. Our results indicate that, even with optimistic estimates of market depth, moderately large macro-shocks may trigger fire sales which lead to substantial losses across bank portfolios, modifying the outcome of bank stress tests. Moreover, we show that price-mediated contagion leads to a heterogeneous cross-sectional loss distribution across banks, which cannot be replicated simply by applying a discount factor to portfolios.

We propose a bank-level indicator, based on liquidity-weighted overlaps of portfolios which can be used to quantify the contribution of a financial institution to price-mediated contagion. Using a granular supervisory dataset, we show that asset class aggregation has a considerable effect on estimated portfolio-level fire sales losses, an issue ignored in the literature so far.

The second part presents an in-depth sensitivity analysis of the Eisenberg & Noe model, a framework commonly used in systemic risk studies. We develop a Taylor series for the clearing vector and characterise it in terms of estimation errors in the liabilities matrix.