  The interconnectedness of financial institutions affects instability and
credit crises. To quantify systemic risk we introduce here the PD model, a
dynamic model that combines credit risk techniques with a contagion mechanism
on the network of exposures among banks. A potential loss distribution is
obtained through a multi-period Monte Carlo simulation that considers the
probability of default (PD) of the banks and their tendency of defaulting in
the same time interval. A contagion process increases the PD of banks exposed
toward distressed counterparties. The systemic risk is measured by statistics
of the loss distribution, while the contribution of each node is quantified by
the new measures PDRank and PDImpact. We illustrate how the model works on the
network of the European Global Systemically Important Banks. For a certain
range of the banks' capital and of their assets volatility, our results reveal
the emergence of a strong contagion regime where lower default correlation
between banks corresponds to higher losses. This is the opposite of the
diversification benefits postulated by standard credit risk models used by
banks and regulators who could therefore underestimate the capital needed to
overcome a period of crisis, thereby contributing to the financial system
instability.
