Average Conditional Volatility: A Measure Of Systemic Risk For Commercial Banks

We propose using the cross-sectional (daily) average conditional volatility of commercial bank stock returns as a measure of systemic risk for the U.S. banking industry. The performance of this measure is tested using data from the 2008 pre-crisis period. The measure is shown to incorporate individual bank risk as well as the cumulative riskiness of a cross-section of banks. Cross-sectional regressions indicate that individual bank’s probability of default is unrelated to the bank’s conditional volatility during times of low, industry wide risk (as measured by average conditional volatility). However, the bank’s conditional volatility significantly affects its probability of default when the industry is experiencing a high level risk. Regardless of the industry level risk, a bank’s probability of default has a significant negative relation with its capital adequacy (as measured by the proportion of equity capital). Additionally, at an aggregate level, Granger causality tests indicate that the conditional volatility of ‘big’ banks causes the riskiness of medium and small banks to increase

Long-Run Underperformance And The Offering Price Clustering Phenomenon

The study proposes a new informational role for the offering price of an equity IPO.  Offering prices are quoted either in whole prices (e.g., $2,$11, $19, etc) or fractional prices (e.g.,$2.35, $11.15,$15.75, etc).  Using Jay R. Ritter’s sample of 1,526 IPOs issued during the period 1975 to 1984, the study examines the relation between the presence of whole price clusters and long-run underperformance.  The results indicate that fractional offering prices are associated with better long-run performance. &nbsp

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Leverage deviation from the target debt ratio and leasing

We find a negative association between the leverage deviation and leasing intensity, implying that firms actively use leasing as a source of financing when faced with a leverage deviation. This negative relation is more pronounced for firms that are underleveraged, are financially constrained, and have a high likelihood of bankruptcy, and weaker for firms with a greater need to preserve debt capacity. We attribute these incentives to distinct features of the lease contract that separate it from secured debt contracts. Overall, our results are consistent with the substitution effects of lease and debt