[We explore whether central bank intervention improves liquidity in the interbank market during the current subprime crisis with unique trade and quote data from the e-MID, the only regulated electronic interbank market in the world. Central bank intervention consistently creates greater uncertainty in the interbank market. Prior to the crisis, the cover-to-bid ratio effectively conveys good and bad news from the central bank, but this link is broken during the crisis, suggesting that standard (and special) interventions that do not specifically target interbank asymmetric information fail to improve market liquidity. Our results suggest that the central bank should release stress tests for individual banks, provide interbank loan guarantees, or engage in direct asset purchases rather than simply providing more capital when counterparty risk poses systemic risk to the interbank market.]

We use data from 2005–2009 that uniquely identify categories of traders to test how speculators such as hedge funds and swap dealers relate to volatility and price changes. In examining various subperiods where price trends are strong, we find little evidence that speculators destabilize financial markets. To the contrary, hedge fund position changes are negatively related to volatility in corn, crude oil, and natural gas futures markets. Additionally, swap dealer activity is largely unrelated to contemporaneous volatility. Our evidence is consistent with the hypothesis that hedge funds provide valuable liquidity and largely serve to stabilize futures markets.

We explore whether central bank intervention improves liquidity in the interbank market during the current subprime crisis with unique trade and quote data from the e-MID, the only regulated electronic interbank market in the world. Central bank intervention consistently creates greater uncertainty in the interbank market. Prior to the crisis, the cover-to-bid ratio effectively conveys good and bad news from the central bank, but this link is broken during the crisis, suggesting that standard (and special) interventions that do not specifically target interbank asymmetric information fail to improve market liquidity. Our results suggest that the central bank should release stress tests for individual banks, provide interbank loan guarantees, or engage in direct asset purchases rather than simply providing more capital when counterparty risk poses systemic risk to the interbank market.

We explore interconnectedness in the interbank overnight lending market and propose the liquidity network and the urgent borrower network which capture the urgency to trade. The liquidity network connects the initiating party in a trade to the passive party, while the urgent borrower network connects passive sellers (lenders) to urgent buyers (borrowers). Along with the buyer/seller trading network, we show these networks complement each other, revealing valuable information that improves short-term forecasts of soft and hard information and country-specific yield spreads. Connectivity increases in these networks during raises volatility and boosts volume, revealing the dual nature of interconnectedness—too much interconnectedness may increase systemic risk, but too little may impede market functioning.

Abstract We study trading and risk management decisions of banks in over-the-counter markets, accounting for 2 types of risk: payoff risk from loans and counterparty risk from trading activities. Our model provides empirically supported predictions on the structure of the interbank credit default swap (CDS) market: i) banks with high default probabilities either buy or sell CDS contracts; ii) because of the counterparty risk friction, payoff risk is only partially shared; and iii) safe banks act as intermediaries and help diversify counterparty risk. Banks manage their default probabilities to become creditworthy counterparties, but they do so in a socially inefficient way.

We study the behavior of the interbank market around the 2008 financial crisis. Using network analysis, we study two network structures, correlation networks based on publicly traded bank returns and physical networks based on interbank lending transactions, among these public and also private banks. While the two networks behave similarly pre-crisis, during the crisis the correlation network shows an increase in interconnectedness, while the physical network highlights a marked decrease in interconnectedness. Moreover, these networks respond differently to monetary and macroeconomic shocks. Physical networks forecast liquidity problems, while correlation networks forecast financial crises.