No abstract

The paper deals with the role that local banks (especially credit cooperative banks) might play in financially supporting the development of technological districts and innovative firms. After introducing the concept and features of technological districts, it focuses on the relations between districts and local banks and between the adoption of innovation and local banking. The central part is an econometric exercise aimed at measuring the weight of high value financial services over the income of a sample of Italian credit cooperative banks. Taking into account the cultural, managerial and organizational requirements of local banks, the work provides insights into how this category of banks can promote innovative financial services to help the development of high‐tech districts and maintain a competitive position in relation to the larger banks.

The global financial crisis in 2008, and the European sovereign debt crisis in 2010, highlighted how credit risk in banking sectors cannot be analysed from a uniquely micro-prudential perspective, focused on individual institutions, but it has instead to be studied and regulated from a macro-prudential perspective, considering the banking sector as a complex system. Traditional risk management tools often fail to account for the complexity of the interactions in a financial system, and rely on simplistic distributional assumptions. In recent years machine learning techniques have been increasingly used, incorporating tools such as text mining, sentiment analysis, and network models in the risk management processes of financial institutions and supervisors. Network theory applications in particular are increasingly popular, as they allow to better model the intertwined nature of financial systems. In this work we set up an analytical framework that allows to decompose the credit risk of banks and sovereign countries in the European Union according to systematic (system-wide and regional) components. Then, the non-systematic components of risk are studied using a network approach, and a simple stress-test framework is set up to identify the potential transmission channels of distress and risk spillovers. Results highlight a relevant component of credit risk that is not explained by common factors, but can still be a potential vehicle for the transmission of shocks. We also show that due to the properties of the network structure, the transmission of shocks applied to different institutions is quite diversified, both in terms of breadth and speed. Our work is useful to both regulators and financial institutions, thanks to its flexibility and its requirement of data that can be easily available.