Liquidity Crunch in the Interbank Market: Is it Credit or Liquidity Risk, or Both

Author(s):  
Angelo S. Baglioni
2018 ◽  
Vol 23 (5) ◽  
pp. 855-892 ◽  
Author(s):  
Elena Carletti ◽  
Agnese Leonello

Abstract We develop a model where banks invest in reserves and loans, and trade loans on the interbank market to deal with liquidity shocks. Two types of equilibria emerge, depending on the degree of credit market competition and the level of aggregate liquidity risk. In one equilibrium, all banks keep enough reserves and remain solvent. In the other, some banks default with positive probability. The latter equilibrium exists when competition is weak and large liquidity shocks are unlikely. The model delivers several implications concerning the relationship between competition, aggregate credit, and welfare.


Author(s):  
Andrea Eross ◽  
Andrew Urquhart ◽  
Simon Wolfe

2016 ◽  
Author(s):  
Andrea Eross ◽  
Andrew Urquhart ◽  
Simon Wolfe

Author(s):  
Pavla Vodová

The recent financial crisis has shown that a liquidity risk plays an important role in the current developed financial system. One of the efficient tools of liquidity risk management is stress testing which can show banks their potential vulnerability to liquidity shocks. The aim of this paper is therefore to measure the liquidity risk sensitivity of Czech commercial banks and to find out the most severe scenario and the most vulnerable bank. Our sample included significant part of the Czech banking sector; we used unconsolidated balance sheet data over the period from 2000 to 2011 which were obtained from annual reports of Czech banks. We have evaluated liquidity risk of each bank in the sample via six different liquidity ratios. Then we stressed these baseline values in three stress scenarios: run on a bank (simulated by a 20% withdrawal of deposits), confidence crisis on the interbank market (simulated by a withdrawal of 20% of interbank deposits) and use of committed loans by counterparties (simulated by a 5% increase of loans provided to nonbank clients). We measured the impact of all scenarios by relative change of liquidity ratios. The impact of modelled liquidity shocks differs among scenarios. The most serious liquidity problems would be caused by the first scenario – run on a bank. The negative influence of third scenario (use of committed loans) is less severe. The confidence crisis on the interbank market would not affect bank liquidity at all. The results also show that the severity of the impact of all scenarios worsens in periods of financial distress. We have also found that large and medium sized banks are most vulnerable to liquidity shocks, mainly to massive deposit withdrawals.


Author(s):  
Valentina Macchiati ◽  
Giuseppe Brandi ◽  
Tiziana Di Matteo ◽  
Daniela Paolotti ◽  
Guido Caldarelli ◽  
...  

AbstractSystemic liquidity risk, defined by the International Monetary Fund as “the risk of simultaneous liquidity difficulties at multiple financial institutions,” is a key topic in financial stability studies and macroprudential policy-making. In this context, the complex web of interconnections of the interbank market plays the crucial role of allowing funding liquidity shortages to propagate between financial institutions. Here, we introduce a simple yet effective model of the interbank market in which liquidity shortages propagate through an epidemic-like contagion mechanism on the network of interbank loans. The model is defined by using aggregate balance sheet information of European banks, and it exploits country and bank-specific risk features to account for the heterogeneity of financial institutions. Moreover, in order to obtain the European-wide topology of the interbank network, we define a block reconstruction method based on the exchange flows between the various countries. We show that the proposed contagion model is able to estimate systemic liquidity risk across different years and countries. Results suggest that our effective contagion approach can be successfully used as a viable alternative to more realistic but complicated models, which not only require more specific balance sheet variables with high time resolution but also need assumptions on how banks respond to liquidity shocks.


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