Suchergebnisse

The framework of Systemically Important Banks (SIBs) was introduced by the financial stability board in the October of 2010 as the institutions "whose disorderly failure, because of their size, complexity and systemic interconnectedness, would cause significant disruption to the wider financial system and economic activity". The current methodology for their determination is based on balance-sheet variables and expert judgment. We propose a cross-sectional statistical procedure based on a permutation test in order to cluster SIBs separating them from the rest of the financial system. This procedure divides the sample in two subsamples choosing a quantile of suitable statistics of the considered variable, in order to reject the null hypothesis of equality in distributions. Our procedure will be applied to the European banking institutions, monitored by EBA, for which this regulator fully discloses information used in the choice of SIFIs done by the Basel committee. The analysis is done considering both single variables and through a weighted combination of them. The results obtained by the methodology we propose, taking into account properly the multivariate features of the decision process, reproduce those done by the Basel Committee for 2015 to identify the group of European Systemically Important Banks. Moreover these results, having a viable statistical methodology to select the SIBs, can open the possibility of extending the selection to a higher frequency framework, by applying the procedure to measure systemic risk, usually available daily.

The cooperation and contamination among mathematicians, statisticians and econometricians working in actuarial sciences and finance are improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas in the form of four- to six-page papers presented at the International Conference MAF2022 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the COVID-19 pandemic, the conference, to which this book is related, was organized in a hybrid form by the Department of Economics and Statistics of the University of Salerno, with the partnership of the Department of Economics of Cà Foscari University of Venice, and was held from 20 to 22 April 2022 in Salerno (Italy) MAF2022 is the tenth edition of an international biennial series of scientific meetings, started in 2004 on the initiative of the Department of Economics and Statistics of the University of Salerno. It has established itself internationally with gradual and continuous growth and scientific enrichment. The effectiveness of this idea has been proven by the wide participation in all the editions, which have been held in Salerno (2004, 2006, 2010, 2014, 2022), Venice (2008, 2012 and 2020 online), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioural finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.