We propose a credit portfolio approach for evaluating systemic risk and attributing it across institutions. We construct a model that can be estimated from high-frequency CDS data. This captures risks from publicly traded banks, privately held institutions, and coöperative banks, extending...

We propose a credit portfolio approach for evaluating systemic risk and attributing it across institutions. We construct a model that can be estimated from high-frequency CDS data. This captures risks from privately held institutions and cooperative banks, extending approaches that rely on...

We address the problem of regulating the size of banks' macroprudential capital buffers by using market-based estimates of systemic risk and by developing a modeling mechanism through which capital buffers can be allocated efficiently across systemic banks. First, a Distance-to-Default type...

We propose a credit portfolio approach for evaluating systemic risk and attributing it across institutions. We construct a model that can be estimated from high-frequency CDS data. This captures risks from privately held institutions and cooperative banks, extending approaches that rely on...

We address the problem of regulating the size of banks' macroprudential capital buffers by using market-based estimates of systemic risk and by developing a modeling mechanism through which capital buffers can be allocated efficiently across systemic banks. First, a Distance-to-Default type...

We propose a simple network–based methodology for ranking systemically important financial institutions. We view the risks of firms –including both the financial sector and the real economy– as a network with nodes representing the volatility shocks. The metric for the connections of the...

We propose a simple network–based methodology for ranking systemically important financial institutions. We view the risks of firms –including both the financial sector and the real economy– as a network with nodes representing the volatility shocks. The metric for the connections of the...

We propose a simple network–based methodology for ranking systemically important financial institutions. We view the risks of firms –including both the financial sector and the real economy– as a network with nodes representing the volatility shocks. The metric for the connections of the...

We estimate the contribution of large U.S, banks to the financial sector systemic risk by using value-at-risk (VaR ), conditional value-at-risk (CoV aR ), and two-stage least square (2SLS) methodology, Our sample is the monthly stock returns of 25 large U.S, banks from 1997 to 2021, We find that...