Systemic Risk and Stability in Financial Networks

Systemic Risk and Stability in Financial Networks

We provide a framework for studying the relationship between the financial network architecture and the likelihood of systemic failures due to contagion of counterparty risk. We show that financial contagion exhibits a form of phase transition as interbank connections increase: as long as the magnitude and the number of negative shocks affecting financial institutions are sufficiently small, more "complete" interbank claims enhance the stability of the system. However, beyond a certain point, such interconnections start to serve as a mechanism for propagation of shocks and lead to a more fragile financial system. We also show that, under natural contracting assumptions, financial networks that emerge in equilibrium may be socially inefficient due to the presence of a network externality: even though banks take the effects of their lending, risk-taking and failure on their immediate creditors into account, they do not internalize the consequences of their actions on the rest of the network.

This study will explore the resilience and stability of financial networks under major shocks, which will be analyzed by reviewing the facts in situations such as the 2008 financial crisis and the research analysis of prominent scholars. One of the main results is that the types of financial networks most susceptible to contagious failures change dramatically as the magnitude or number of negative shocks exceeds a certain threshold. In particular, more financial connectivity is no longer a guarantee of stability. Conversely, interbank liabilities can fuel financial contagion and create a more vulnerable system in the event of a large shock. The results show that in large-scale shocks, “weakly connected” financial networks - for example, those consisting of a pair of connected banks that share a minimal amount of assets and liabilities with the rest of the system - are much more stable than more complete networks. This paper puts forward the consideration of financial network stability, which helps financial networks maintain stability in the face of potential financial shocks to a certain extent.

The rapid expansion of Financial Technology (FinTech) is fundamentally reshaping financial systems, yet its role as a source of systemic risk and its dynamic connectedness with traditional energy and macroeconomic markets remain critically underexplored. This paper employs an integrated time-frequency framework to model financial spillover networks and demonstrates its utility in analyzing the connectedness between emerging FinTech sub-sectors, energy markets, and macroeconomic uncertainty. Using the Diebold and Yilmaz (2012) spillover index in the time domain and the Baruník and Křehlík (2018) spectral decomposition in the frequency domain, we uncover a highly interconnected system: total connectedness reaches 43.62% for returns and 40.65% for volatility, showing that price shocks propagate more strongly than risk shocks. During the COVID-19 period, interconnectedness surged above 70%, highlighting how external shocks intensify contagion. We find that key FinTech indices such as Kensho Future Payments, KBW FinTech, and Kensho Alternative Finance act as major net transmitters, while the Distributed Ledger index, geopolitical risk, U.S. policy uncertainty, Brent oil, and U.S. 10-year Treasury yields are net receivers, signaling that within the financial network, shock propagation is now led by FinTech rather than emanating primarily from traditional macroeconomic indicators. Frequency results add important insight: volatility spillovers are mainly short-term (44.57%), reflecting transient fear contagion, while return spillovers are more persistent. Overall, our findings challenge the macro-driven spillover view and offer a time-sensitive framework for effective hedging and regulation. FinTech emerges as a key short-term shock transmitter, with clear implications for investors’ hedging strategies and regulators’ systemic risk monitoring.

Climate change-induced pan-financial market and the contagion of systemic financial risks are becoming important issues in the financial sector. The paper measures the temperature difference in terms of the degree and direction of deviation of the actual temperature relative to the average temperature of the same historical period. Based on the high-dimensional time-series variable LASSO-VAR-DY framework, we construct a pan-financial market volatility correlation network consisting of 112 Chinese listed companies in banking, insurance, securities, real estate, traditional energy, and new energy, use eigenvector centrality to measure the systematic risk of each firm, and then empirically test the effect of temperature difference on systematic risk under pan-financial market scenario. The results of the study show that (ⅰ) There is a significant difference among the systemic risk of financial sectors such as banking, insurance, and securities in the financial market pan-financial market scenario and the systemic risk when the financial market pan-financial market is not taken into account;(ⅱ) Higher temperature significantly exacerbates systemic financial risk, while colder temperature significantly mitigates systemic risk, but both have an asymmetric effect on systemic risk, and there is sectoral heterogeneity.(ⅲ) From the dynamic evolutionary characteristics, there are significant differences in the response of systemic financial risk to positive and negative temperature shocks;(iv) The results of the systemic risk variance decomposition indicate that the temperature change contributes more to the variance of systemic risk in the banking and securities sectors in pan-financial market;(ⅴ) The contagion source of financial systemic risk shows an obvious path of leaping and changing characteristics, and the contagion source of systemic risk (source of impact) shows the evolution law of "bank → real estate → new energy → temperature difference," which means that the temperature difference has become the contagion source of systemic financial risk. This study provides a reference for preventing and resolving systemic risks under pan-financial market scenario and provides a basis for improving the current macroprudential regulatory framework.

Transmission channels of systemic risk and contagion in the European financial network

Transmission Channels of Systemic Risk and Contagion in the European Financial Network

Macro-Prudential Regulation from the Perspective of the Financial Network

Financial markets are exposed to systemic risk (SR), the risk that a major fraction of the system ceases to function, and collapses. It has recently become possible to quantify SR in terms of underlying financial networks where nodes represent financial institutions, and links capture the size and maturity of assets (loans), liabilities and other obligations, such as derivatives. We demonstrate that it is possible to quantify the share of SR that individual liabilities within a financial network contribute to the overall SR. We use empirical data of nationwide interbank liabilities to show that the marginal contribution to overall SR of liabilities for a given size varies by a factor of a thousand. We propose a tax on individual transactions that is proportional to their marginal contribution to overall SR. If a transaction does not increase SR, it is tax-free. With an agent-based model (ABM) (CRISIS macro-financial model), we demonstrate that the proposed ‘Systemic Risk Tax’ (SRT) leads to a self-organized restructuring of financial networks that are practically free of SR. The SRT can be seen as an insurance for the public against costs arising from cascading failure. ABM predictions are shown to be in remarkable agreement with the empirical data and can be used to understand the relation of credit risk and SR.

The global financial system has become highly connected and complex. It has been proved in practice that existing models, measures and reports of financial risk fail to capture some important systemic dimensions. Recently, advisory boards have been established at a high level, and regulations are being directly targeted at systemic risk. In the same direction, a growing number of researchers employ network analysis to model systemic risk in financial networks. Current approaches are concentrated on interbank payment network flows at national and international levels. This work builds on existing approaches to propose systemic risk assessment at the micro level. The proposed model (partially) captures the systemic aspect of credit risk of bank customers and argues that this part of risk is neglected in both credit scoring models and interbank systemic risk calculations. In particular, the analysis of intra-bank financial risk interconnections is introduced by examining the real case of a ‘receivables-as-collateral’ network. In the data set examined, an initial failure of five customers, representing 17 per cent of the network’s total value, results in the subsequent failure of 15 customers, representing 41 per cent of the total value. The author’s model could be complementary to existing credit scoring models that account for mainly idiosyncratic customers’ financial profiles. Private or public organisations could further elaborate the specification to include a wider range of parameters, such as transactions on contracts, assets and cash flows. Identification of the systemic risk could be beneficial for a business entity in assessing unexplored sources of risks in its portfolios of assets and customers. Understanding and modelling these ‘particles’ of risk could enable more realistic monitoring and the provision of early warning messages for market supervising bodies.

This paper proposes a model of the dynamics of credit contagion through non-performing loans on financial networks. Credit risk contagion is modeled in the context of the classical SIS (Susceptibles-Infected-Susceptibles) epidemic processes on networks but with a fundamental novelty. In fact, we assume the presence of two different classes of infected agents, and then we differentiate the dynamics of assets subject to idiosyncratic risk from those affected by systemic risk by adopting a SIIS (Susceptible-Infected1-Infected2-Susceptible) model. In the recent literature in this field, the effect of systemic credit risk on the performance of the financial network is a hot topic. We perform numerical simulations intended to explore the roles played by two different network structures on the long-term behavior of assets affected by systemic risk in order to analyze the effect of the topology of the underlying network structure on the spreading of systemic risk on the structure. Random graphs, i.e., the Erdös–Rényi model, are considered “benchmark” network structures while core-periphery structures are often indicated in the literature as idealized structures, although they are able to capture interesting, specific features of real-world financial networks. Moreover, as a matter of comparison, we also perform numerical experiments on small-world networks.

This paper measures the joint default risk of financial institutions by exploiting information about counterparty risk in credit default swaps (CDS). A CDS contract written by a bank to insure against the default of another bank is exposed to the risk that both banks default. From CDS spreads we can then learn about the joint default risk of pairs of banks. From bond prices we can learn the individual default probabilities. Since knowing individual and pairwise probabilities is not sufficient to fully characterize multiple default risk, I derive the tightest bounds on the probability that many banks fail simultaneously.

Under growing uncertainty and interdependence, systemic risks are essential for the effective functioning of the global financial system. Therefore, the subject of the proposed study is systemic risks for the global financial system. The goal of this work is to identify and disclose the role of systemic risks in carrying out investment activities. The article solves the following objectives: to identify and reveal key features and characteristics of systemic risks, to identify new challenges in systemic risk management, to identify new manifestations of systemic risks. To achieve the goal of the study, the following methods are used: system-structural, synergetic, method of comparative analysis, method of analysis and synthesis. The study reveals the following results. The main approaches to defining the concept of systemic risks are identified and their comparative analysis is carried out. The main approaches to measuring systemic risks and measurement criteria are identified. The differences between the concepts of systemic and systematic risk are revealed, and the mechanism of their interrelation is identified. New systemic risks in the conditions of global uncertainty are identified. The impact of the COVID-19 pandemic on systemic risks is determined. The main new types of risks and threats to financial stability in the long run are identified. The main directions of response of financial regulatory bodies to new systemic risks are determined. The main effects of the impact of measures to stimulate economic growth on the state of financial markets and investment activities are identified. The conclusions of the study are as follows. It is determined that there is no unanimous definition of systemic risk. Key features of systemic risks are identified, such as unpredictability, large-scale impact, spillover effect, impact on the real sector of the economy, etc. It is determined that when measuring systemic risk there are two problems: the measure of quantitative expression of systemic risk as a unit and the distribution of systemic risk between individual financial institutions. It is revealed that systemic risk can be a source of systematic risks. The COVID-19 pandemic, as an extraordinary macroeconomic shock, is belived to lead to new systemic risks. It is revealed that new types of systemic risks include, in particular, default risks, complexity of the macroeconomic environment, risks of sovereign financing, risk of lack of liquidity. The impact of new systemic risks on investment activities is revealed, in particular, changes in the business models of financial institutions, changes in the strategies of investment funds, lower ratings of debt securities, increasing the cost of debt financing, lack of liquidity.

The drivers of systemic risk in financial networks: a data-driven machine learning analysis

This note outlines the approach of the proposed revision to the Institutional View (IV) when assessing whether systemic financial stability risks are elevated due to foreign currency (FX) mismatches. The approach builds on the staff guidance regarding risk assessments in bilateral surveillance, while allowing for flexibility to draw on future advances in best practice. This note proposes a two-step approach to assess systemic risks from FX mismatches. This note is organized as follows. Section II outlines the sources of systemic risks stemming from FX debt and potential amplification channels. Section III outlines the risk assessment approach in practice and Section IV concludes.