We develop an empirically based simulation study to test two types of policies designed to control systemic risk: preventive policies targeting capital requirements and mitigation policies targeting default resolution. We find that capital buffers reduce both the number of defaults and the resulting losses. The loss reduction benefit increases as the magnitude of adverse shocks becomes higher. We find that a simple branch-breakup resolution strategy reduces the loss borne by the Federal Deposit Insurance Corporation (FDIC). The mitigation effect becomes higher as the fraction of assets resolved through auctions and auction competitiveness increase.

Timely identification and anticipation of adverse conditions in the financial system are critical for macroprudential policy. However, there is no consensus on how to evaluate the quality of systemic measures. This paper provides a framework to compare measures of systemic conditions. We illustrate the proposed tests with a case study of US measures from 1976 to 2013. We find that measures which include information from multiple markets improve identification of critical system states. However, tested measures show limited capacity to anticipate critical episodes.