Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. In 2014 all ECB publications feature a motif taken from the €20 banknote. Terms of use: Documents innote: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. AbstractWe use an extensive data set of bilateral exposures on credit default swap (CDS) to estimate the impact on collateral demand of new margin and clearing practices and regulations. We decompose collateral demand for both customers and dealers into several key components, including the "velocity drag" associated with variation margin movements. We demonstrate the impact on collateral demand of more widespread initial margin requirements, increased novation of CDS to central clearing parties (CCPs), an increase in the number of clearing members, the proliferation of CCPs of both specialized and non-specialized types, and client clearing. Among other results, we show that system-wide collateral demand is increased significantly by the application of initial margin requirements for dealers, whether or not the CDS are cleared. Given these dealer-to-dealer initial margin requirements, however, mandatory central clearing is shown to lower, not raise, system-wide collateral demand, provided there is no significant proliferation of CCPs. Central clearing does, however, have significant distributional consequences for collateral requirements across various types of market participants.JEL codes : G20, G28, G15.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractThis paper analyses the network structure of the credit default swap (CDS) market, using a unique sample of counterparties' bilateral notional exposures to CDS on 642 sovereign and financial reference entities. We study the network structure, similarly to the literature on interbank and payment systems, by computing a variety of network metrics at the aggregated level and for several subnetworks. At a reference entity level, we analyse the determinants of some key network properties for large reference entities. Our main results, obtained on a sub-sample of 191 reference entities, are the following. First, the CDS network shows topological similarities with the interbank network, as we document a "small world" structure and a scale-free degree distribution for the CDS market. Second, there is considerable heterogeneity in the network structures across reference entities. In particular, the outstanding debt volume and its structure (maturity, collateralization), the riskiness, the type (sovereign/financial) and the location (European/non-European) of reference entities significantly influence the size, the activity and the concentration of the CDS exposure network. For instance, the network on a high-volatility reference entity is typically more active, larger in size and less concentrated. JEL Codes: G15.Keywords: Credit Default Swap (CDS), financial networks, network topology, network determinants. 2 Non-technical summaryEven though the CDS market has grown considerably over the last decade, its structure is still little described and understood. This paper uses a large and novel dataset to analyze the CDS exposures network from three different perspectives: (i) the aggregated CDS network, (ii) various sub-networks, such as the sovereign CDS network and (iii) networks for CDS reference entities. The dataset, obtained from the DTCC, comprises virtually all gross and net exposures worldwide on 642 reference entities, including 40 sovereign and 602 financial reference entities as of end-2011. Its coverage represents about 32.7% of the global singlename CDS market.This paper provides two main contributions to the literature on CDS markets. First, it uses the tools of network analysis to provide a characterization of the topological properties of the aggregated CDS network and of several sub-networks with a lower level of aggregation (e.g. sovereign vs. financial, European vs. non-European reference entities). ...

We empirically explore the fragility of wholesale funding of banks, using transaction‐level data on short‐term, unsecured certificates of deposit in the European market. We do not observe a market‐wide freeze during the 2008 to 2014 period. Yet, many banks suddenly experience funding dry‐ups. Dry‐ups predict, but do not cause, future deterioration in bank performance. Furthermore, during periods of market stress, banks with high future performance tend to increase reliance on wholesale funding. We therefore fail to find evidence consistent with adverse selection models of funding market freezes. Our evidence is in line with theories highlighting heterogeneity between informed and uninformed lenders.

2 According to the BIS' Derivative Statistics (December 2014), financial institutions account for more than 97% of all gross derivatives exposures. Financial institutions' derivatives positions for hedging include, in addition to interest rate and foreign exchange derivatives, equity derivatives (0.7%) and commodity derivatives (0.1%). Not included in these calculations are credit derivatives, as no breakdown between uses for hedging and trading is available.