between banks created unprecedented problems in the world economy. In this paper we analyze the detailed trading data from the European (e-MID) interbank market to better understand how interbank trading reflected these economic problems. We construct and examine physical networks of trade that allow us to examine bank connectedness over time. Further, we compare and contrast correlation networks (constructed with Granger-causality between stock returns) with physical networks (constructed from interbank trades) to better interpret results from each. We demonstrate that correlation and physical networks reflect important, but different, economic conditions in the European banking sector. During the crisis, physical bank networks reveal a breakdown in connectivity in the interbank market. Interestingly, correlation networks show increased comovements in market returns during the crisis that have been interpreted as an increase in connectivity, a connectivity that we ascribe to a common factor unrelated to interbank trading. Moreover, correlation and physical networks respond differently to monetary and macroeconomic shocks. Interconnectedness in physical networks adjust strongly and quickly to central bank operations and to announcements of new information, revealing important markers of liquidity at short (daily) horizons. Conversely, while interconnectedness in correlation networks marks the onset of the crisis, this metric changes little in response to central announcements and interventions. Our results demonstrate that correlation networks can identify (and forecast) periods of impending financial crises. Complementarily, physical interbank trading networks serve to identify weakening interconnectedness in the interbank system
between banks created unprecedented problems in the world economy. In this paper we analyze the detailed trading data from the European (e-MID) interbank market to better understand how interbank trading reflected these economic problems. We construct and examine physical networks of trade that allow us to examine bank connectedness over time. Further, we compare and contrast correlation networks (constructed with Granger-causality between stock returns) with physical networks (constructed from interbank trades) to better interpret results from each. We demonstrate that correlation and physical networks reflect important, but different, economic conditions in the European banking sector. During the crisis, physical bank networks reveal a breakdown in connectivity in the interbank market. Interestingly, correlation networks show increased comovements in market returns during the crisis that have been interpreted as an increase in connectivity, a connectivity that we ascribe to a common factor unrelated to interbank trading. Moreover, correlation and physical networks respond differently to monetary and macroeconomic shocks. Interconnectedness in physical networks adjust strongly and quickly to central bank operations and to announcements of new information, revealing important markers of liquidity at short (daily) horizons. Conversely, while interconnectedness in correlation networks marks the onset of the crisis, this metric changes little in response to central announcements and interventions. Our results demonstrate that correlation networks can identify (and forecast) periods of impending financial crises. Complementarily, physical interbank trading networks serve to identify weakening interconnectedness in the interbank system
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