  We model systemic risk using a common factor that accounts for market-wide
shocks and a tail dependence factor that accounts for linkages among extreme
stock returns. Specifically, our theoretical model allows for firm-specific
impacts of infrequent and extreme events. Using data on the four sectors of the
U.S. financial industry from 1996 to 2011, we uncover two key empirical
findings. First, disregarding the effect of the tail dependence factor leads to
a downward bias in the measurement of systemic risk, especially during weak
economic times. Second, when these measures serve as leading indicators of the
St. Louis Fed Financial Stress Index, measures that include a tail dependence
factor offer better forecasting ability than measures based on a common factor
only.
