This paper examines the pricing of volatility risk and idiosyncratic volatility in the cross-section of corporate bond returns for the period of 1994–2016. Results show that bonds with high volatility betas have low expected returns, and this negative relation appears in all segments of corporate bonds. Further, bonds with high idiosyncratic bond (stock) volatility have high (low) expected returns, and this relation strengthens as ratings decrease. Conventional risk factors and bond/issuer characteristics cannot account for these cross-sectional relations. There is evidence that the effect of idiosyncratic stock volatility on expected bond returns works through the channel of contemporaneous stock returns.

Many US states have a single cutoff date for school entry, meaning that some children are older than others when they begin kindergarten. We show that this variation in birth months is associated with differences in adult labor market outcomes in the mutual fund industry. Relatively older managers (i.e., those born just after the cutoff) make better stock selections, and their funds outperform their younger peers’ funds by 0.48% per annum. This difference is linked to increased confidence. Survey respondents judge relatively older managers as appearing more confident in photographs, and these managers display more confident behavior: making larger bets, window dressing their holdings less, and securing more fund flows conditional on performance.

We study the behavior of the interbank market around the 2008 financial crisis. Using network analysis, we study two network structures, correlation networks based on publicly traded bank returns and physical networks based on interbank lending transactions, among these public and also private banks. While the two networks behave similarly pre-crisis, during the crisis the correlation network shows an increase in interconnectedness, while the physical network highlights a marked decrease in interconnectedness. Moreover, these networks respond differently to monetary and macroeconomic shocks. Physical networks forecast liquidity problems, while correlation networks forecast financial crises.