This paper defines asymptotic marginal expected shortfall (AMES) for banks within a financial system and provides corresponding estimation method based on multivariate extreme value theory. The estimation method does not assume a specific dependence structure among bank equity returns. Both theoretical AMES and the estimator possess additive property and thus can serve as a tool to allocate system-wide risk to individual institutions. We apply the AMES to 30 global systemically important financial institutions (G-SIFIs). We show that the AMES outperforms the MES in predicting extreme losses during extreme systemic events. By taking the AMES as the reference point for allocating systemic risk to individual institutions, we show that an allocation according to simple bank characteristics such as size and individual risk can be imperfect. The allocation unfairness of individual risk or size across all the G-SIFIs has increased since 2008.

This paper documents and analyzes the gender gap in the online credit market. Using data from Renrendai, a leading peer-to-peer lending platform in China, we show that lending to female borrowers is associated with better loan performance, including a lower probability of default, a higher expected profit, and a lower expected loss than for their male peers. However, despite the higher creditworthiness, we don't find any measurable gender impact on funding success rate, meaning that female borrowers have to compensate lenders by providing higher profitability to achieve a similar funding probability. These evidences indicate the existence of a gender gap that discriminate against female borrowers. Further analysis implies that this gender gap is independent of the amount of information disclosed by borrowers.