  This work proposes an augmented variant of DebtRank with uncertainty
intervals as a method to investigate and assess systemic risk in financial
networks, in a context of incomplete data. The algorithm is tested against a
default contagion algorithm on three ensembles of networks with increasing
density, estimated from real-world banking data related to the largest 227 EU15
financial institutions indexed in a stock market. Results suggest that DebtRank
is capable of capturing increasing rates of systemic risk in a more sensitive
and continuous way, thereby acting as an early-warning signal. The paper
proposes three policy instruments based on this approach: the monitoring of
systemic risk over time by applying the augmented DebtRank on time snapshots of
interbank networks, a stress-testing framework able to test the systemic
importance of financial institutions on different shock scenarios, and the
evaluation of distribution of systemic losses in currency value.
