Abstract
The Basel Committee and the Financial Stability Board require a consensus on the identification of characteristics that make a financial institution more prone than others to be severely hit by systemic shocks. This paper introduces a new tool to achieve this goal: a model for the Conditional Average Systemic Effects (CASE). The CASE quantifies the average effect of a system wide shock or market downturn on the profit and loss account of a bank, a firm or on the return of an asset. We propose a linear model for CASE with heterogeneous effects in observable characteristics. These models complement alternative measures of systemic risk and allow researchers to identify the determinants of the vulnerability of a given financial institution. We develop bootstrap inference that accounts for both estimation risk and model misspecification risk, and show the utility of our results in Monte Carlo simulations and an empirical application to 100 large U.S. financial firms.
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