Structure Of Financial Network Research Articles

Post global financial crisis multiplex financial network structure longevity: Does it predict re-globalization? Empirical investigation for 234 countries

Research background: International capital flows show decreasing trend after each financial crisis as countries become unwilling to invest in risky counterparties. Recent discussions over structural changes within the global financial network suggest that its structure may be still undergoing some changes. Uncovered weaknesses of over-connectedness with unstable un-ally countries during global financial crisis were further highlighted by recent Covid-19 pandemic. However, previous research suggest that important structural changes happened after global financial crisis. Thus, such situation raises a question whether post global financial crisis multiplex network structure is long-term and is it exhibiting re-globalization pattern as suggested by re-globalization megatrend. Purpose of the article: The main objective of the paper is to assess post global financial crisis multiplex financial network structure longevity and to identify whether it exhibits re-globalization pattern. Methods: Multiplex financial network mapping, network topological analysis and structure longevity methods were employed to evaluate network connectedness, topological structural properties of multiplex financial network and its’ structure longevity. 5-layer multiplex financial network was mapped for different types of capital flows for each layer: net direct and portfolio investments in debt, equity and net banking assets for each year in the post global financial crisis period 2009–2020. Each network layer employed bilateral data from up to 234 countries of the world. Next step of research employed a set of network-level measures defining topological features of each network in the period of 2009–2020 in order to capture the trend of structural changes and evidence of re-globalization. The final stage analyzes multiplex financial network structure by layer and country longevity. Findings & value added: Analysis reveals that stock (positions) multiplex financial network aggregate connectedness, i.e., density and value, increased during post- global financial crisis period until Covid-19 pandemic period, thus, suggesting that globalization in terms of stock is continuing. Topological structural properties of multiplex financial network changed as number of strongly and weakly connected countries decreased and more countries became included in network. Longevity analysis of layer and country structure within multiplex financial network reveal that banking and debt investments are increasing since 2018, while equity part is decreasing. Developed countries, i.e., the United Kingdom and Germany, decrease in multiplex financial network structure, while developing, especially from Asia region (i.e., Hong Kong SAR, China and Singapore) increase. Such empirical results support the re-globalization megatrend in terms of investment type and counterparty structure as highlighted by most recent scientific discussion showing that it started early after global financial crisis. Research also uncovers some regional trends of Asia region increasing in investment network structure, while Europe decreasing. As developing countries attract more and more investment and further develop, global cost saving and, thus, returns from global investment into these countries may be decreasing, resulting in lower gain from globalization. Our analysis may facilitate investment strategy decisions by suggesting that even though gains from globalization might be decreasing, instead of localization, countries could follow ally investment strategy to manage the risk of over-dependency from unstable counterparty while preserving globalization wealth.

Financial network structure and systemic risk

For systemic risk, the impact of the financial network's characteristics remains imperfectly understood at best, even if the view that network structure is closely related to systemic risk has become a broad consensus. By choosing S&P 500 constituents as the research sample, we investigate the structural characteristics of the Engle-Granger networks and explore the impact of network centrality on one-quarter-ahead systemic risk. We find that a firm's network centrality is positively related to both dimensions of its systemic risk (i.e. the firm's vulnerability to, and contribution to, system-wide downturns). The results remain robust after we consider the potential endogeneity and various sensitivity checks. An examination of potential channels reveals that centrally located firms in the network have a high extent of co-movement with the market, and are likely to trigger systemic market failures caused by stock price crashes in clusters once they fall into a downturn. We further show that the positive relation between network centrality and future systemic risk is more salient for financial firms and more pronounced during recessions.

Strategic default in financial networks

This paper investigates a model of default in financial networks where the decision by one agent on whether or not to default impacts the incentives of other agents to escape default. Agents' payoffs are determined by the clearing mechanism introduced in the seminal contribution of Eisenberg and Noe (2001). We first show the existence of a Nash equilibrium of this default game. Furthermore, we develop an algorithm to find all Nash equilibria and guide regulatory intervention that relies on the financial network structure. The algorithm provides a ranking for the set of Nash equilibria for specific financial network structures, which can serve as a measure of systemic risk. Finally, we show that introducing a central clearing counterparty achieves the efficient equilibrium at no additional cost.

Multilayer Financial Complex Networks and Their Applications

In the context of global integration, the theory of financial complex networks has made significant contributions to the establishment of stable financial systems and effective regulatory systems. This survey paper presents a systematic methodology for the multi-layer financial complex networks and their applications. Several typical financial networks in existing research are first summarized: the interbank network, the credit network, the international trade network, and the dealer network. The main methods used in the existing literature to analyze the structure of financial networks include maximum entropy methods, network characterization, community detection, and dynamic multi-layer network. Financial risk contagion as well as spillover effects are the core issues that most of the literature focuses on. Finally, this paper reviews the shortcomings of the existing literature and suggests future research directions in this area.

Systemic Risk Analysis of Multi-Layer Financial Network System Based on Multiple Interconnections between Banks, Firms, and Assets.

Global financial systems are increasingly interconnected, and risks can spread more easily, potentially causing systemic risks. Research on systemic risk based on multi-layer financial networks is relatively scarce, and studies usually focus on only one type of risk. This paper develops a model of the multi-layer financial network system based on three types of links: firm-bank credit, asset-bank portfolio, and interbank lending, which simulates systemic risk under three risk sources: firm credit default, asset depreciation, and bank bankruptcy. The impact of the multi-layer financial network structure, default risk threshold, and bank asset allocation strategy is further explored. It has been shown that the larger the risk shock, the greater the systemic risk under different risk sources, and the risk propagation cycle tends to rise and then decline. As centralized nodes in the multi-layer financial network system, bank nodes may play both blocking and propagation roles under different risk sources. Furthermore, the multi-layer financial network system is most susceptible to bank bankruptcy risk, followed by firm credit default risk. Further research indicates that increasing the average degree of firms in the bank–firm credit network, the density of the bank-asset portfolio network, and the bank capital adequacy ratio all contribute to reducing systemic risk under the three risk sources. Additionally, the more assets a bank holds in a single market, the more vulnerable it is to the risks associated with that market.

Financial networks a retrospective view to looking ahead

This study defines the cognitive structure of financial networks research by looking at the terms scholars have used over the last 31 years of research. Applying co-word analysis over a sample of 680 records extracted from Scopus database, the findings reveal that that the literature on financial networks is made up of six thematic clusters, these are: Dynamics of financial networks, Complex banking networks, Risk and contagion in financial networks, Topology and structure of financial networks, Main methodologies and techniques and Post-crisis responses. In addition to laying the groundwork for understanding financial networks through a retrospective view, this study shows how bibliometrics (especially co-words analysis) can contribute to reducing conceptual ambiguity in emerging research fields, such as financial networks research, and open doors for future research.

Network structure of urban digital financial technology and its impact on the risk of commercial banks

<abstract> <p>In the context of the development of digital finance, the complexity of the network formed by urban digital financial technology has been deepening. Based on Chinese city data from 2010 to 2019, this paper conducts a dynamic evaluation of urban digital financial technology through grey target theory and uses social network analysis methods to study the network structure characteristics of urban digital financial technology and its impact on commercial bank risks. The study found that the spatial network of urban digital financial technology shows a trend of complexity and closeness, developed cities occupy a central position in the network of digital financial technology linkages and are net spillovers of urban digital financial technology. Further research on the impact of urban digital financial network structure on commercial bank risk found that both the overall network structure of urban digital financial technology and individual network structure have a significant inhibiting effect on commercial bank risk. Therefore, this paper focuses on the balanced development of digital financial technology in cities, while seeking to further exert the demonstration role of developed cities and achieve the reduction of risk level of commercial banks through the increase of overall network density and the decrease of network efficiency and network hierarchy.</p> </abstract>

Diversity in brokerage cities: the evolution of urban positionality in China’s financial system

ABSTRACT The competitiveness of financial centres increasingly hinges on their capability at brokering financial flows. Though existing studies have paid attention to the rise and fall of some financial brokerage cities (FBCs), less is known about how such dynamics of FBCs have been shaped by the structure of financial networks and the interactions among FBCs. Taking China’s financial network as an example, we consider not only the interactions between FBCs but also their multiple brokerage roles. Empirical analyses confirm that a city’s brokerage dynamics are neither independent nor can be portrayed as a linear trajectory.

Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

Using the topological structure of financial networks to build a portfolio has attracted a wide range of research interests. A similarity matrix based on the technical indicators (TIs), and a correlation matrix based on the stock returns, are used to construct the financial networks. Hybrid topological measures are calculated for both networks to select the stocks that are further used to build portfolios. The random matrix theory (RMT) tool is used to filter the risk measurement in the Markowitz optimization process. A general result is presented in this study, which indicates that the portfolios composed of peripheral stocks consistently outperform those composed of central stocks. As shown in the empirical results, the RMT method can improve the Markowitz optimization process and further improve the performance of portfolios. By comparing the different portfolios established under different networks, the results reveal the advantage of the topological measure based on TIs. This study provides an important approach for constructing financial networks from the time series data, and it explores the impact of different similarity measurements on the network-based models for portfolio selection.

What is the minimal systemic risk in financial exposure networks?

We quantify how much systemic risk can be eliminated in financial contract networks by rearranging their network topology. By using mixed integer linear programming, financial linkages are optimally organized, whereas the overall economic conditions of banks, such as capital buffers, total interbank assets and liabilities, and average risk-weighted exposure remain unchanged. We apply the new optimization procedure to 10 snapshots of the Austrian interbank market where we focus on the largest 70 banks covering 71% of the market volume. The optimization reduces systemic risk (measured in DebtRank) by about 70%, showing the huge potential that changing the network structure has on the mitigation of financial contagion. Existing capital levels would need to be scaled up by a factor of 3.3 to obtain similar levels of DebtRank. These findings underline the importance of macro-prudential rules that focus on the structure of financial networks. The new optimization procedure allows us to benchmark actual networks to networks with minimal systemic risk. We find that simple topological measures, like link density, degree assortativity, or clustering coefficient, fail to explain the large differences in systemic risk between actual and optimal networks. We find that if the most systemically relevant banks are tightly connected, overall systemic risk is higher than if they are unconnected.

Measuring Systemic Risk Contagion Effect of the Banking Industry in China: A Directed Network Approach

ABSTRACT To capture the impact of investor sentiment on risk contagion of financial institutions and potential tail risks caused by financial network structure, this paper uses a directed network approach to measure systemic risk contagion effect of Chinese banking industry. We use linear quantile lasso regression and local polynomial method to estimate TENET model, and construct a weighted directed network. Moreover, we study directed network from different perspectives, analyze financial risk contagion effect and the influence of investor sentiment on financial risk contagion, and identify systemically important financial institutions. We find that: (1) As crisis spreads, financial system becomes more closely related, and total network connectivity continues to rise until it reaches a maximum value. (2) Total network connectivity and systemic risk have the same upward or downward trend, but systemic risk lags behind total network connectivity. (3) Current bank has characteristics of “too big to fail” and “too contact to fail”.

Contagion, Not Only Interconnection: Measuring the Transmission of Financial Distress

This paper proposes a novel approach to understand contagion of financial distress in the banking system, which takes into account the spatial nature of the phenomena. We use a Bayesian spatial autoregressive model that treats the likelihood of default of each bank as endogenous, and dependent on the network formed by all the other banks. Identification is achieved by controlling for bank fundamentals, latent macrofinancial and bank specific shocks that have similar consequences to contagion and act as confounding factors. Through the lens of a simulations exercise we study the importance of the structure of financial networks for financial stability, shedding light on the empirical adherence of important theoretical propositions that remain untested.

The Founding of the Federal Reserve, the Great Depression, and the Evolution of the U.S. Interbank Network

Financial network structure is an important determinant of systemic risk. This article examines how the U.S. interbank network evolved over a long and important period that included two key events: the founding of the Federal Reserve and the Great Depression. Banks established connections to correspondents that joined the Federal Reserve in cities with Fed offices, initially reducing overall network concentration. The network became even more focused on Fed cities during the Depression, as survival rates were higher for banks with more existing connections to Fed cities, and as survivors established new connections to those cities over time.

Dynamic interbank network analysis using latent space models

Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model, each node has an unobserved temporal trajectory in a low-dimensional Euclidean space. Model parameters and latent banks’ positions are estimated within a Bayesian framework. We apply this methodology to analyze two different datasets: the unsecured and the secured (repo) interbank lending networks. We show that the model that incorporates a latent space performs much better than the model in which the probability of a tie depends only on observed characteristics; in particular, the latent space model is able to capture the core-periphery structure of financial networks quite well, whereas the model without a latent space is unable to do so.

The general dynamic risk assessment for the enterprise by the hologram approach in financial technology

The purpose of this paper is to discuss the general risk assessment under the Hologram framework for the enterprise based on big data language; and to illustrate the Hologram as a new tool for establishing a mechanism to evaluate SMEs growth and change in financial technology dynamically (here we mainly focus on SMEs as they are one of the very important classes for enterprises with less information available from financial accounting report and associated assets. Indeed, the approach discussed here is applicable to general enterprises).The key idea of our new approach is to introduce and use the “Hologram” (similar to, “holographic portrait” used in portrait holography), a platform for data fusion dynamically, as a tool and mechanism to describe the dynamic evolution of SMEs based on their business dynamic behavior. Through processing structured and/or unstructured data in terms of “related-party” information sets which analyze (1) “investment” and (2) “management” information provided by SMEs’ business behavior, and extracting “Risk Genes” from complex financial network structures in the business ecosystem, we can establish a “good” or “bad” rating for SMEs by using data fusion dynamically and financial technology. This method to assess SMEs is a new approach to evaluating SMEs’ development dynamically based on the network structure information of enterprise and business behavior. The framework introduced in this paper for the dynamic mechanism of SMEs’ development and evolution allows us to assess the risk of any SMEs (in particular to evaluate SMEs’ loan applications) even not available for critical data required in traditional finance analysis including information such as financial accounting and associated assets, etc. This new “Hologram” approach for SMEs assessment is a pioneering innovation that incorporates big data and financial technology for inclusive financial services in practical application. Ultimately, the Hologram approach offers a new theoretical solution for the long-standing problem of credit risk assessment for SMEs and individuals in practice.Since the information embedded in SMEs’ business behavior reveals the competition and cooperation mechanism that drives its stochastic resonance (SR) behavior which is associated with successful SMEs development, the two concepts of SAI and URR under the Hologram approach to risk assessment that identifies if an SME is “good” are based on the network generated from an SMEs’ related-party information in terms of “investment” and “management” dynamically, along with other available information such as related investment capital and risk control. Significantly, the Hologram approach to risk assessment for SMEs does not require critical data of traditional financial account and related assets, etc. which heavily depend on financial accounting and associated assets used by financial risk analysis in practice. Using big data and FinTech Hologram method discussed in this paper utilizes the related-party information (in term of investment and management) of each SME which exists in an embedded business network to overcome the situation for SMEs which always have not or have not enough in providing accounting and associated asset information in the practice.By the feature of each Hologram for a given SME, one always has the related-party information in terms of either investment, or management dynamically, which is indeed also an explanation for the reason why the new approach proposed only comes true only until the era of big data’s occurring by using ideas from financial technology today.Furthermore, this paper explores the implementation of the “Holo Credit Loan”, a pure credit loan without any collateral and guarantee launched in 2016, as practical applications of the Hologram approach. We illustrate the framework of SMEs risk assessment under the Holograms new theoretical basis for solving the long-standing problem of credit risk assessment for SMEs (and individuals). Moreover, this paper’ conclusion will address the performance of the “Holo Credit Loan”.

The structure of financial networks, and Western Balkan banking systems

This paper analyses the structure of the part of the global financial network that the banking systems of the Western Balkan countries belong to for the period 2007-2013. The data it uses is mostly from BIS consolidated banking statistics and the methodological tools used are based on network theory. It finds that a few features of the pan-European financial network strongly influence the likelihood of risk transfer to the Western Balkan area. First, the banking systems of the six Western Balkan countries are periphery nodes in the financial network. Second, the network is highly concentrated around a small number of nodes and all other nodes are weakly interconnected. Third, there are three nodes or regional common lenders that have dominant roles in the Western Balkan banking systems: the banking systems of Austria, Italy, and Greece. The conclusion is that in the case of financial distress anywhere in the financial network, risk could easily spread to Western Balkan banking systems, with the interconnections in the financial network having the effect of a risk transmitter. Wherever in the network the initial shock is sufficiently strong, financial contagion could easily spread to the Western Balkan area through the regional common lenders.

The Founding of the Federal Reserve, the Great Depression and the Evolution of the U.S. Interbank Network

Financial network structure is an important determinant of systemic risk. This paper examines how the U.S. interbank network evolved over a long and important period that included two key events: the founding of the Federal Reserve and the Great Depression. Banks established connections to correspondents that joined the Federal Reserve in cities with Fed offices, initially reducing overall network concentration. The network became even more focused on Fed cities during the Depression, as survival rates were higher for banks with more existing connections to Fed cities, and as survivors established new connections to those cities over time.

The Dynamical Mechanism for SMEs Evolution Under the Hologram Approach

The Dynamical Mechanism for SMEs Evolution Under the Hologram Approach

The missing links: A global study on uncovering financial network structures from partial data

Capturing financial network linkages and contagion in stress test models are important goals for banking supervisors and central banks responsible for micro- and macroprudential policy. However, granular data on financial networks is often lacking, and instead the networks must be reconstructed from partial data. In this paper, we conduct a horse race of network reconstruction methods using network data obtained from 25 different markets spanning 13 jurisdictions. Our contribution is two-fold: first, we collate and analyze data on a wide range of financial networks. And second, we rank the methods in terms of their ability to reconstruct the structures of links and exposures in networks.

State and Network Structures of Stock Markets Around the Global Financial Crisis

We consider the effects of the 2008 global financial crisis on the global stock market before, during, and after the crisis. We generate complex networks from a cross-correlation matrix such as the threshold network (TN) and the minimal spanning tree (MST). In the threshold network, we assign a threshold value by using the mean and standard deviation of cross-correlation coefficients. When the threshold is equal to the mean of these coefficients, we observe a giant cluster composed of three economic zones in all three periods. We find that during the crisis, the countries in the Asian zone were weakly connected and those in the American zone were tightly linked to the countries in the European zone. At a large threshold, the three economic zones were fragmented. The European countries connected tightly, but the Asian countries bound weakly. The MST constructed from the distance matrix. In the MST, France remained a hub node in all three periods. The size of the MST shrank slightly during the crisis. We observe a scaling relation between the network distance of nodes from the central hub (France) and the geometrical distance. We observe the topological change of the financial network structure during the global financial crisis. The TN and MST are complementary roles to understand the connecting structure of financial complex networks. The TN reveals to observe the clustering effects and robustness of the cluster during the financial crisis. The MST shows the central hub and connecting node among the economic zones.