Risk attribution and interconnectedness in the EU via CDS data

Abstract

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.

Aplicación de Machine Learning en la Gestión de Riesgo de Crédito Financiero: Una revisión sistemática

“…Asimismo, es importante determinar el riesgo de crédito desde una perspectiva macroprudencial considerando al sector bancario como un sistema complejo y no como instituciones individuales (Giacometti et al, 2020).…”

Data-Driven Approach for Systemic Risk: A Macroprudential Perspective

“…with φ i indicating the default time and { λi , λj } the marginal sovereign default intensities of obligors {i, j }. Following the modelling dependence framework in [1,6], we consider a common factor F and estimate its intensity λF,ij as…”

“…In the case of sovereign, this plays a fundamental role for systemic risk measurement and assessment. In line with [6], this paper considers 8 European countries belonging to Northern Europe (e.g. Germany, France, Netherlands, UK-Northern EU) and Southern Europe (e.g.…”

“…The estimates would need further investigation to disentangle the underlying components, both from an economic and mathematical perspectives. A Marshall-Olkin copula model [5] could be used to further explore the interplay between the sovereign default risk and the dynamics of the banking sector, as in [6], together with non-systematic components. Moreover, a comparison with alternative benchmark models for the dependence structure would be desirable.…”

“…The paper proposes a data-driven approach to analyse the dynamics of sovereign CDS and systemic risk. Starting from the works by [1] and [6], a datadriven approach applied to CDS quotes is considered to estimate a reduced form model for the marginal intensity of defaults at country level. This information is then used to investigate the presence of common factors across economies for 8 European countries.…”