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Stefano Battiston

22 December 2016
WORKING PAPER SERIES - No. 33
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Abstract
We develop a framework to analyse the Credit Default Swaps (CDS) market as a network of risk transfers among counterparties. From a theoretical perspective, we introduce the notion of flow-of-risk and provide sufficient conditions for a bow-tie network architecture to endogenously emerge as a result of intermediation. This architecture shows three distinct sets of counterparties: i) Ultimate Risk Sellers (URS), ii) Dealers (indirectly connected to each other), iii) Ultimate Risk Buyers (URB). We show that the probability of widespread distress due to counterparty risk is higher in a bow-tie architecture than in more fragmented network structures. Empirically, we analyse a unique global dataset of bilateral CDS exposures on major sovereign and financial reference entities in 2011 −2014. We find the presence of a bow-tie network architecture consistently across both reference entities and time, and thatt the flow-of-risk originates from a large number of URSs (e.g. hedge funds) and ends up in a few leading URBs, most of which are non-banks (in particular asset managers). Finally, the analysis of the CDS portfolio composition of the URBs shows a high level of concentration: in particular, the top URBs often show large exposures to potentially correlated reference entities.
JEL Code
G10 : Financial Economics→General Financial Markets→General
G15 : Financial Economics→General Financial Markets→International Financial Markets
29 March 2017
WORKING PAPER SERIES - No. 2041
Details
Abstract
We develop a framework to analyse the Credit Default Swaps (CDS) market as a network of risk transfers among counterparties. From a theoretical perspective, we introduce the notion of flow-of-risk and provide sufficient conditions for a bow-tie network architecture to endogenously emerge as a result of intermediation. This architecture shows three distinct sets of counterparties: i) Ultimate Risk Sellers (URS), ii) Dealers (indirectly connected to each other), iii) Ultimate Risk Buyers (URB). We show that the probability of widespread distress due to counterparty risk is higher in a bow-tie architecture than in more fragmented network structures. Empirically, we analyse a unique global dataset of bilateral CDS exposures on major sovereign and financial reference entities in 2011 - 2014. We find the presence of a bow-tie network architecture consistently across both reference entities and time, and that the flow-of-risk originates from a large number of URSs (e.g. hedge funds) and ends up in a few leading URBs, most of which are non-banks (in particular asset managers). Finally, the analysis of the CDS portfolio composition of the URBs shows a high level of concentration: in particular, the top URBs often show large exposures to potentially correlated reference entities.
JEL Code
G10 : Financial Economics→General Financial Markets→General
G15 : Financial Economics→General Financial Markets→International Financial Markets
20 November 2019
WORKING PAPER SERIES - No. 2331
Details
Abstract
We study the interplay between two channels of interconnectedness in the banking system. The first one is a direct interconnectedness, via a network of interbank loans, banks' loans to other corporate and retail clients, and securities holdings. The second channel is an indirect interconnectedness, via exposures to common asset classes. To this end, we analyze a unique supervisory data set collected by the European Central Bank that covers 26 large banks in the euro area. To assess the impact of contagion, we apply a structural valuation model NEVA (Barucca et al., 2016a), in which common shocks to banks' external assets are reflected in a consistent way in the market value of banks' mutual liabilities through the network of obligations. We identify a strongly non-linear relationship between diversification of exposures, shock size, and losses due to interbank contagion. Moreover, the most systemically important sectors tend to be the households and the financial sectors of larger countries because of their size and position in the financial network. Finally, we provide policy insights into the potential impact of more diversified versus more domestic portfolio allocation strategies on the propagation of contagion, which are relevant to the policy discussion on the European Capital Market Union.
JEL Code
C45 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Neural Networks and Related Topics
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages