Systemic risk assessment through high order clustering coefficient

In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order $l$ of a node $i$ introduced in Cerqueti et al. (2018). Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks.Comment: Submitte

Reengineering of interbank networks

We investigate the reengineeering of interbank networks with a specific focus on capital increase. We consider a scenario where all other components of the network’s infrastructure remain stable (a practical assumption for short-term situations). Our objective is to assess the impact of raising capital on the network’s robustness and to address the following key aspects. First, given a predefined target for network robustness, our aim is to achieve this goal optimally, minimizing the required capital increase. Second, in cases where a total capital increase has been determined, the central challenge lies in distributing this increase among the banks in a manner that maximizes the stability of the network. To tackle these challenges, we begin by developing a comprehensive theoretical framework. Subsequently, we formulate an optimization model for the network’s redesign. Finally, we apply this framework to practical examples, highlighting its applicability in real-world scenarios

Operational research and artificial intelligence methods in banking

Supplementary materials are available online at https://www.sciencedirect.com/science/article/pii/S037722172200337X?via%3Dihub#sec0031 .Copyright © 2022 The Authors. Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

We analyse systemic risk in the core global banking system using a new network-based spectral eigen-pair method, which treats network failure as a dynamical system stability problem. This is compared with market price-based Systemic Risk Indexes, viz. Marginal Expected Shortfall, Delta Conditional Value-at-Risk, and Conditional Capital Shortfall Measure of Systemic Risk in a cross-border setting. Unlike paradoxical market price based risk measures, which underestimate risk during periods of asset price booms, the eigen-pair method based on bilateral balance sheet data gives early-warning of instability in terms of the tipping point that is analogous to the R number in epidemic models. For this regulatory capital thresholds are used. Furthermore, network centrality measures identify systemically important and vulnerable banking systems. Market price-based SRIs are contemporaneous with the crisis and they are found to covary with risk measures like VaR and betas

ESSAYS ON INTERBANK NETWORKS AND SYSTEMIC RISK IN THE EUROPEAN UNION BANKING SECTOR

The financial crisis of 2008-2009 and the subsequent EU sovereign debt crisis of 2010-2012 highlighted the vulnerability of the banking sector to contagious defaults, and so mainly due to the interconnectedness of banks through short-term and mostly unsecured interbank loans. Links created in the interbank markets are a potential channel for propagating contagion especially when banks default on their interbank financial obligations. In addition, banks' investments in correlated assets, the valuation of counterparty risk, and banks' diversification into non-traditional sources of revenue are potential channels amplifying contagion. This thesis, therefore, examines factors that contribute to systemic risk in the European Union banking sector. First, we investigate systemic risk and dynamics of contagion within the European Union interbank market using data on 168 to 249 commercial banks from 2011 to 2018. A copula approach is used to reconstruct the network. We then apply a duplex interbank network model to test the banks' sensitivity to capitalisation and interbank lending. We show that the European Union interbank market is robust in mitigating the propagation of contagious defaults from direct bilateral exposure. However, when banks’ liquidate their external assets, correlation in asset prices amplifies contagion, becoming a significant threat to the stability of the banking union. In addition, an increase in the banks’ capitalisation and a reduction in their engagement in interbank lending significantly reduce systemic risk. Next, we examine the impact of credit valuation on systemic risk using a sample of 112 to 238 commercial banks from 2005 to 2020. We reconstruct the EU interbank networks using a minimum density approach. We then test for the significance of credit valuation under a range of shock sizes and recovery rates, using a Network of Equity Valuation (NEVA) model. We find that credit valuation plays a significant role in the stability of the EU banking system. At lower levels of valuation, contagion is only evident when a significant shock hits the banking system. In contrast, as credit valuation increases, banks become sensitive to valuation such that they default even in the absence of shocks. We observe that, as more banks default in the first round, the amplification of contagious defaults reduces to a minimum. Finally, we investigate the role of income diversification on systemic risk contribution. We construct five market-based measures of systemic risk widely used by regulators using a common framework that allows for comparisons. Using an unbalanced panel of 101 publicly traded banks headquartered in the EU from 2000 to 2019, we find that non-interest income, bank size, loan loss provision, and leverage are significant contributors to systemic risk. In contrast, liquidity and banks’ charter size significantly reduce systemic risk. By decomposing non-interest income into its main components, we observe that commissions and fees and other non-interest income are the key drivers of systemic risk. When controlling for size, we find that other non-interest income, commissions and fees, and total assets are the main contributors to banks’ systemic risk for large, medium, and small banks, respectively. Overall, our findings suggest that measures such as increased capital requirements as proposed in Basel III and the enactment of the Bank Recovery and Resolution Directive (BRRD) have the potential to significantly enhance financial stability in the EU. Similarly, enhanced disclosure requirements for banks in relation to the reporting of credit value adjustments, as revised in the Pillar 3 framework of the Basel Committee, is a major step towards a more robust banking union. However, our results also suggest that bank managers, regulators, and policymakers may wish to remain mindful of other important factors, such as non-interest income, bank size, and loan loss provision, which we find may also have severe destabilising effects on the EU banking system

Robust and sparse banking network estimation

Network analysis is becoming a fundamental tool in the study of systemic risk and financial contagion in the banking sector. Still, the network structure must typically be estimated from noisy and aggregated data, as micro data on the status quo banking network structure are often unavailable, or the true network is unobservable. Graphical models can help researchers to infer network structures, but they are often criticized for relying too heavily on unrealistic assumptions. They also tend to yield dense structures that are difficult to interpret. Here, we propose the tlasso model for estimating sparse banking networks. The tlasso captures the conditional dependence structure between banks through partial correlations, and estimates sparse networks in which only the relevant links are identified. The model also accounts for the non-Gaussianity of financial data and it is robust to outliers and model misspecification. Our empirical analysis focuses on estimating the dependence structure of a sample of European banks from credit default swap data. We observe that the presence of communities in the banking network plays an important role in terms of systemic risk and contagion dynamics. We also introduce a decomposition of strength centrality that allows us to better characterize the role of each bank in the network and to identify the most relevant channels for the transmission of financial distress.Web of Science2701655