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Risk capital is an important input for management functions. Capital structure decisions, capital budgeting, and ex post performance measurement require different measures of risk capital. While it has become common to estimate risk capital using VaR models, it is not clear that VaR‐based capital estimates are optimal for applications to management functions (e.g. risk management, capital budgeting, performance measurement, or regulation). This article considers three typical problems that require an estimate of credit risk capital: an optimal equity capital allocation; an optimal capital allocation for capital budgeting decisions; and an optimal capital allocation to remove moral hazard incentives from a compensation contract based on ex post performance. The optimal credit risk capital allocation is different for each problem and is never consistent with a credit VaR estimate of unexpected loss. The results demonstrate that the optimal risk capital allocation depends on the objective.

Based on a wide survey of over 1,500 Italian customers, analyses both payment services and sales and private banking areas. In the former, human contact attributes of service prove to be overemphasized when customer satisfaction is observed. In the latter, technology seems extremely important in helping bank branch officers decide which new services to offer to which customers. In both sections customers’ clusters are identified according to the importance of different service attributes; subsequently, they are described in terms of demographic, behavioural and psychographic characteristics. Technology‐oriented unsatisfied customers seem to belong to most segments. This calls for a major shift in the approach to delivery channels the majority of Italian banks are still adopting.

Stresses that recent changes in financial markets have involved the payment system and the banking processes directly devoted to short term forecasting. Proposes that financial flows control systems must be adopted that can measure performance and liquidity risks consistent with the models often used for credit and market risks.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

The COVID-19 outbreak has undoubtedly affected overall mental health. Thus, researching resilience is important, as it has been previously discussed as a means to protect people from mental health problems. This study aims to clarify whether survivors of a traumatic event (i.e. cancer survivors) are more resilient to living through another traumatic experience, such as COVID-19, compared to those who have never had such an experience. The study also examines the role of emotional creativity in this process.

A quantitative research design was adopted. The data collection was performed through a survey (N = 338), which was conducted among two separate groups of participants. The first group (N = 152) included the survivors of a traumatic event (i.e. cancer survivors), and the second group (N = 186) included those who did not have such an experience.

The results demonstrate that living through a traumatic experience results in a higher level of resilience during another traumatic experience (i.e. COVID-19), which is the result of higher post-traumatic growth. Moreover, emotional creativity is discussed as an explanatory variable that explains a significantly higher level of post-traumatic growth among survivors of a traumatic event.

This research offers a better understanding of the effect of living through a traumatic event on post-traumatic growth and resilience in living through another traumatic experience. Moreover, post-traumatic growth is explained through emotional creativity improvement, which happens after experiencing a traumatic life event.

This study aims to investigate the effect of immigration on housing prices in Australia both at the national and regional levels.

Data for eight Australian states on a quarterly basis from 2004–2017 is used. To study the possible dynamic and endogenous relationship between housing prices and immigration, a panel vector autoregressive error correction model (PVECM) is adopted.

Analysis of the results indicates that in the short run immigration positively and significantly affects housing prices, whereas in the long run no significant relationship was observed between the two variables. From the regional breakdown and analysis, it is discerned that in some states there is a significant and positive effect of immigration on residential real estate prices in the long run. Causality analysis confirms that the direction of causation is from immigration to housing prices.

The study illustrates that immigration and interstate migration, as well as high salaries, have been causing a rise in housing demand and subsequently housing prices. To monitor exceedingly high housing prices, local authorities should be controlling migration and salary levels.

Past research studies had highlighted the importance of native interstate migration in explaining the nexus between immigration – housing prices. In this study, it has been empirically verified how immigration has been affecting the locational decisions of natives and subsequently how this has been affecting housing prices.