Italy’s banking sector, a mosaic of diversity, is dissected in a recent study that delves into the nuances of Probability of Default (PD) modeling. The intricacies of size, operations, and ownership structures create a landscape that demands careful consideration. This exploration aims to mirror the Italian banking panorama, offering insights gleaned from a meticulous dataset encompassing 1,059 balance sheets of 100 banks over the period 2010-2020.
Sample Construction and Diversity: The study’s foundation lies in a deliberate selection strategy to ensure diversification within the sample. The resulting 72 performance indicators serve as explanatory variables, capturing the essence of Italy’s banking dynamics. Noteworthy is the inclusion of both individual banks and group leaders, with 27 banking groups and 10 cooperative banks contributing to the richness of the dataset.
Bank Size and Distribution: The size spectrum, spanning from small cooperatives to major banking institutions, underscores the diverse nature of Italy’s banking sector. The five largest banks, holding over half of the market share, are identified as systemically important. Interestingly, the distribution of total assets paints a picture of both very small and large banks, emphasizing the need for tailored PD models that consider this dichotomy.
Outliers and Data Cleansing: The study adopts a nuanced approach to outliers, recognizing their relevance in the context of banks undergoing default processes. The classification of outliers, including specific criteria like capital ratio, ensures a balance between data purity and model effectiveness. The resulting dataset, after outlier identification, presents a distribution approaching a bell-shaped form.
Preliminary Data Analysis: The default rate, a critical metric in PD modeling, reveals a low default portfolio scenario, constituting 2.6% annually. An intriguing bimodal distribution suggests a degree of default correlation within this portfolio. The study also explores the relationship between GDP growth and default rates, highlighting the complex interplay between economic factors and bank failures.
PD Drivers and Model Exclusions: A glimpse into conventional PD drivers indicates that stability is associated with moderate size, substantial capital, higher profitability, and notable profit growth. Linear trends dominate, prompting the exclusion of squared factor values from the PD model.
Conclusion: As the study peels back the layers of Italy’s banking sector, it becomes evident that a one-size-fits-all approach to PD modeling falls short. The need for tailored models that consider the diverse landscape, outliers, and unique characteristics of Italian banks emerges as a key takeaway. This exploration paves the way for future research, promising a more nuanced understanding of Italy’s financial intricacies.
Senior Business Analyst with a track record of spearheading international operations between Europe, America, UAE, and Asia. Specializing in risk management and financial modeling in the financial services sector. Fluent in English, Italian (native), with basic knowledge of German and French. Seeking a role to apply strategic insights and leadership skills.