To be bailed out or to be left to fail? A dynamic competing risks hazard analysis

Abstract

During the late 2000s financial crisis, a large number of banks either failed or received financial aid thus inflicting substantial losses on the system. We contribute to the early warning literature by developing a dynamic competing risks hazard model that explores the joint determination of the probability of a distressed bank to face a licence withdrawal or to be bailed out. The underlying patterns of distress are analysed based on a broad range of bank-level and environmental factors. We find that institutions with inadequate capital, illiquid and risky assets, poor management, low levels of earnings and high sensitivity to market conditions have a higher probability to go bankrupt. Bailed out banks, on the other hand, face both capital and liquidity shortages, experience low earnings, and are highly exposed to market products; however, neither managerial expertise, nor the quality of assets are relevant to the odds of bailout. We further document that large and complex banks are less likely to fail and more likely to be bailed out and that authorities are more prone to provide support to a distressed bank, which is well-connected with politicians and political parties and less prone to let it go bankrupt. Importantly, our model outperforms the commonly used logit model in terms of forecasting accuracy in all the in-and out-of-sample tests we conduct.JEL Classification: C13; C53; D02; G01; G21.

Debt Holder Monitoring and Implicit Guarantees: Did the BRRD Improve Market Discipline?

“…The time inconsistency problem at play is fairly simple: Even though ex-ante supervisors would have preferred to let banks fail, ex-post the risks and social costs associated with letting them fail seemed to justify the bailout. Letting banks enter bankcruptcy procedures, it is often argued, is not a subgame perfect strategy for the supervisor as there are states of the world in which ex-post she prefers a bail-out (Acharya and Yorulmazer (2007), DeYoung et al (2013) and Papanikolaou (2018)). This "implicit guarantee" weakens debt holders incentives to monitor bank risk taking in the first place.…”

Do bank bailouts have an impact on the underwriting business?

“…To address the potential self-selection bias arising from being a bailed-out bank, we employ a two-stage Heckman selectivity model (Heckman, 1979). Here, we first estimate a probit model, regressing the Recapitalized dummy on all control variables from our main specification, as well as on certain determinants discussed in the literature on bank recapitalizations (Beccalli & Frantz, 2016;Berger & Bouwman, 2013;Gerhardt & Vennet, 2017;Mariathasan & Merrouche, 2012;Papanikolaou, 2018). From this probit estimation, we obtain the inverse Mills ratio, which is used as one of the regressors in the second-stage equation to produce consistent estimates of underwriters' market shares.…”

Debt Holder Monitoring and Implicit Guarantees: Did the BRRD Improve Market Discipline?

“…The time inconsistency problem at play is fairly simple: Even though ex-ante supervisors would have preferred to let banks fail, ex-post the risks and social costs associated with letting them fail seemed to justify the bailout. Letting banks enter bankcruptcy procedures, it is often argued, is not a subgame perfect strategy for the supervisor as there are states of the world in which ex-post she prefers a bail-out (Acharya andYorulmazer (2007), DeYoung et al (2013) and Papanikolaou (2018)). This "implicit guarantee" weakens debt holders incentives to monitor bank risk taking in the first place.…”