The Ho-Saunders model (1981) is extended to consider the case of loan heterogeneity. Pure interest spreads may be reduced when cross-elasticities of demand between bank products are considered. The resulting diversification benefits emanate from the interdependence of demands across bank services and products—a type of portfolio effect. Control over relative rate spreads, across product types, and the resulting ability to manipulate the arrival of transactions demands enables the financial intermediary to maintain a more active role in managing its inventory risk exposure.

This paper investigates the primary and secondary syndicated bank loan market to analyze the effect on pricing when the financial institution commingles syndicated lending with merger advisory services. In particular, we investigate the connection between the acquirer’s choice of financial advisor in a merger and future financing commitments. We find evidence of underpricing of syndicated bank loans in both the primary and secondary market. In the primary market, we show that non-acquisition loans granted by merger advisors to acquiring firms after the merger announcement date are charged a lower all-in-spread relative to acquisition loans if there has been a prior lending relationship. Consistent with this finding, we find that syndicated bank loans for non-acquisition purposes arranged by the acquirer’s advisor after the merger announcement date trade in the secondary market at a significant discount. Since the terms on these non-acquisition loans are not set upon merger announcement, they are most subject to risk shifting and underpricing agency problems. These findings offer evidence consistent with the existence of loss leader and potentially conflicted loans (priced at below-market terms) that are offered by the acquirer’s relationship bank advisor in order to win merger advisory business.

The traditional structure-conduct-performance (SCP) hypothesis relates concentration and the mean level of prices but ignores the dispersion of prices around their mean levels. This paper tests two hypotheses which are capable of explaining both the mean and the dispersion of elements in the pricing vector of retail banks—a concentration/collusion-based hypothesis and an asymmetric information-based hypothesis. Evidence is found that the dispersion of bank fees across banks may be due to both market structure and information reasons. The level of bank fees appears to be generally insignificantly related to concentration.

Contingent capital (coco) automatically recapitalizes the banking system during financial crises if the trigger mechanism is properly designed. We propose a dual trigger mechanism based on: (1) aggregate systemic risk in the banking system, measured using CATFIN, and (2) the individual bank’s contribution to overall systemic risk, measured using delta CoVaR. The dual trigger is highly correlated with system-wide insolvency risk and prices systemic risk. We set different triggers for banks, insurance companies and broker-dealers. Using the 99% cut-off, systemic coco issued by Lehman and Bear Stearns would have been triggered in November 2007, months prior to their actual demise.

To avoid problems associated with Jensen's inequality, we use a corrected inefficiency measure to compare the estimate of inefficiency obtained using the distribution free (DF) method with the estimate obtained using the stochastic cost frontier (SCF). The results of the paper are unchanged. The DF method yields measures of inefficiency that are significantly higher than measures obtained using the SCF.

After exogenous demand shocks caused by natural disasters, FinTech lenders are more responsive to increased demand for reconstruction mortgages than traditional banks and non-FinTech shadow banks. Both FinTech and traditional banks increase credit supply, but FinTech supply is more elastic without increases in risk-adjusted interest rates or delinquency rates. Comparing lending supply channels, banks respond to regulatory incentives to lend to damaged areas, whereas FinTech lenders supply more credit when traditional banks rely more on balance sheet financing and physical branch networks. Compared to traditional banks, FinTech lenders increase supply elasticity more aggressively in response to local competitive pressure.