In this work we develop advanced techniques for measuring bank insolvency risk. More specifically, we contribute to the existing body of research on the Z-Score. We develop bias reduction strategies for state-of-the-art Z-Score measures in the literature. We introduce novel estimators whose aim is to effectively capture nonstationary returns; for these estimators, as well as for existing ones in the literature, we discuss analytical confidence regions. We exploit moment-based error measures to assess the effectiveness of these estimators. We carry out an extensive empirical study that contrasts state-of-the-art estimators to our novel ones on over ten thousand banks. Finally, we contrast results obtained by using Z-score estimators against business news on the banking sector obtained from Factiva. Our work has important implications for researchers and practitioners. First, accounting for the degree of nonstationarity in returns yields a more accurate quantification of the degree of solvency. Second, our measure allows researchers to factor in the degree of uncertainty in the estimation due to the availability of data while estimating the overall risk of bank insolvency.JEL-Classification: C20, C60, G21

Traditional credit risk models adopt the linear correlation as a measure of dependence and assume that credit losses are normally-distributed. However some studies have shown that credit losses are seldom normal and the linear correlation does not give accurate assessment for asymmetric data. Therefore it is possible that many credit models tend to misestimate the probability of joint extreme defaults. This paper employs Copula Theory to model the dependence across default rates in a credit card portfolio of a large UK bank and to estimate the likelihood of joint high default rates. Ten copula families are used as candidates to represent the dependence structure. The empirical analysis shows that, when compared to traditional models, estimations based on asymmetric copulas usually yield results closer to the ratio of simultaneous extreme losses observed in the credit card portfolio. Copulas have been applied to evaluate the dependence among corporate debts but this research is the first paper to give evidence of the outperformance of copula estimations in portfolios of consumer loans. Moreover we test some families of copulas that are not typically considered in credit risk studies and find out that three of them are suitable for representing dependence across credit card defaults.

This paper investigates the drivers of systemic risk and contagion among European banks. First, we use copulas to estimate the systemic risk contribution and systemic risk sensitivity based on CDS spreads of European banks from 2005 to 2014. We then run panel regressions for our systemic risk measures using idiosyncratic bank characteristics and country control variables. Our results comprise highly significant drivers of systemic risk in the European banking sector and have important implications for bank regulation. We argue that banks which receive state aid and have risky loan portfolios as well as low amounts of available liquid funds contribute most to systemic risk whereas relatively poorly equity equipped banks, mainly engaged in traditional commercial banking with strong ties to the local private sector, headquartered in highly indebted countries are most sensitive to systemic risk.

The aim of this paper is to propose a survival credit risk model that jointly accommodates three types of time-to-default found in bank loan portfolios. It leads to a new framework that extends the standard cure rate model introduced by Berkson & Gage [8] regarding the accommodation of zero-inflations. In other words, we propose a new survival model that takes into account three different types of individuals which have so far not been jointly accounted for: (i) an individual with an event at the starting time (zero time); (ii) non-susceptible for the event, or (iii) susceptible for the event. Considering this, the zero-inflated Weibull non-default rate regression models, which include a multinomial logistic link for the three classes, are presented using an application for credit scoring data. The parameter estimation is reached by the maximum likelihood estimation procedure and Monte Carlo simulations are carried out to assess its finite sample performance.

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This paper compares four commonly used systemic risk metrics using data on U.S. financial institutions over the period 2005-2014. The four systemic risk measures examined are the (i) marginal expected shortfall, (ii) codependence risk, (iii) delta conditional value at risk, and (iv) lower tail dependence. Our results demonstrate that the alternative measurement approaches produce very different estimates of systemic risk. Furthermore, we show that the different systemic risk metrics may lead to contradicting assessments about the riskiness of different types of financial institutions. Overall, our findings suggest that systemic risk assessments based on a single risk metric should be approached cautiously.

This paper compares four commonly used systemic risk metrics using data on U.S. financial institutions over the period 2005-2014. The four systemic risk measures examined are the (i) marginal expected shortfall, (ii) codependence risk, (iii) delta conditional value at risk, and (iv) lower tail dependence. Our results demonstrate that the alternative measurement approaches produce very different estimates of systemic risk. Furthermore, we show that the different systemic risk metrics may lead to contradicting assessments about the riskiness of different types of financial institutions. Overall, our findings suggest that systemic risk assessments based on a single risk metric should be approached cautiously.