Structure Of Financial Network Research Articles

Central Clearing Valuation Adjustment

This paper develops an XVA (costs) analysis of centrally cleared trading, parallel to the one that has been developed in the last years for bilateral transactions. We introduce a dynamic framework that incorporates the sequence of cash-flows involved in the waterfall of resources of a clearing house. The total cost of the clearance framework for a clearing member, called CCVA for central clearing valuation adjustment, is decomposed into a CVA corresponding to the cost of its losses on the default fund in case of defaults of other member, an MVA corresponding to the cost of funding its margins and a KVA corresponding to the cost of the regulatory capital and also of the capital at risk that the member implicitly provides to the CCP through its default fund contribution. In the end the structure of the XVA equations for bilateral and cleared portfolios is similar, but the input data to these equations are not the same, reflecting different financial network structures. The resulting XVA numbers differ, but, interestingly enough, they become comparable after scaling by a suitable netting ratio.

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. JEL Classification: G20, L14, D85, C63

Uncertainty in an Interconnected Financial System, Contagion, and Market Freezes

This paper studies contagion and market freezes caused by uncertainty in financial network structures. Our model demonstrates that in a financial system where financial institutions are interconnected, a negative shock to an individual financial institution could spread to other institutions, causing market freezes because of creditors' uncertainty about the financial network structure. Our model also reveals that when both a large creditor and a continuum of small creditors are present, the size of the large creditor will affect the severity of market freezes substantially. Moreover, our model is used to examine central bank policies to alleviate market freezes.

Financial networks, bank efficiency and risk-taking

Networks with a core–periphery topology are found in many financial systems across different jurisdictions. Though the theoretical and structural aspects of core–periphery networks are clear, the consequences that core–periphery structures bring for banking efficiency stand as an open question. We address this gap in the literature by providing insights as to how the structure of financial networks can affect bank efficiency. We find that core–periphery structures are cost efficient for banks, which is a characteristic that encourages the participation of banks in financial networks. On the downside, we also show that core–periphery structures are risk-taking inefficient, because they imply higher systemic risk levels in the financial system. In this way, regulators should be aware of the excessive risk inefficiency that arises in the financial system due to individual decisions made by banks in the network.

Key Borrowers Detection by Long-Range Interactions

We propose a new method for assessing agents' influence in financial network structures, which takes into consideration the intensity of interactions. A distinctive feature of this approach is that it considers not only direct interactions of agents of the first level and indirect interactions of the second level, but also long-range indirect interactions. At the same time we take into account the attributes of agents as well as the possibility of impact to a single agent from a group of other agents. This approach helps us to identify systemically important elements which cannot be detected by classical centrality measures or other indices. The proposed method was used to analyze the banking foreign claims for the end of 1Q 2015. Under the approach, two types of key borrowers were detected: a) major players with high ratings and positive credit history; b) intermediary players, which have a great scale of financial activities through the organization of favorable investment conditions and positive business climate.

Finding modules and hierarchy in weighted financial network using transfer entropy

We study the modular structure of financial network based on the transfer entropy (TE). From the comparison with the obtained modular structure using the cross-correlation (CC), we find that TE and CC both provide well organized modular structure and the hierarchical relationship between each industrial group when the time scale of the measurement is less than one month. However, when the time scale of the measurement becomes larger than one month, we find that the modular structure from CC cannot correctly reflect the known industrial classification and their hierarchy. In addition the measured maximum modularity, Qmax, for TE is always larger than that for CC, which indicates that TE is a better weight measure than CC for the system with asymmetric relationship.

Network Centrality, Failure Prediction and Systemic Risk

A financial market can be expressed in a network structure where the stocks resides as nodes and the links account for returns correlation. Centrality measure in the financial network structure captures firms’ embeddedness and connectivity in the capital market structure. This paper investigates firms’ centrality in the financial network as an explanatory variable in corporate failure prediction and also as a systemic risk measure. First, when analyzing the CDS spreads, I find peripheral firms in the network to have higher average CDS spreads and higher propensity to CDS jump events. Second, centrality is found to increase the explanatory power of default prediction models and moreover, it is negatively related to failure and bankruptcy probability. This implies that peripheral firms in the network are more likely to fail. Finally, examining the out-of-sample performance of centrality as a systemic risk measure, I find that centrality distinguish correctly the firms that suffered a higher loss during the 2007/2008 crisis period.

Entropy of Global Financial Linkages

This paper proposes network entropy as a tool for measuring diversity of highly connected financial networks. The computation of network entropy hinges on eigenvector centrality and Shannon entropy. Two policy-related findings emerge from this research. First, regarding time variation of network entropy, international diversification of the global financial network constructed from foreign claims of international banks has retreated since the financial crisis of 2007-2008. Foreign claims among 20 reporting countries have become more concentrated on core countries such as US and UK since 2009. The change is more vividly captured by network entropy with an unprecedented drop of the measure. This finding illustrates that network entropy is a more acute measure for diversity of a financial network due to the inclusion of information about financial network structures captured by eigenvector centrality. Second, on the subject of financial linkages, the results reveal sizable regional heterogeneity. In both developing Asia &Pacific area and developing Africa & Middle East area, there is a negative correlation between volatility of changes in foreign claims and international diversification of funding sources measured by funding entropy. In other words, more international diversification is related with lower volatility. Especially in case of developing Europe, however there is no evidence for the relation. This paper also finds interesting regional variations in the relation between international diversification of lending and that of borrowing.

Modelling as The Development Instrument of Commodity and Financial Networks in Regional Economy

The article is devoted to investigations of the relationship of commodity-production and financial network structures in the regional economy as dual conjugate systems. Material flows (raw materials, goods, etc.) are circulating in the commodity network according to the Leontyev, input-output model. Nonmaterial flows of property rights, money, etc. in the financial network are reflecting the movement of material objects. Network structure is considered to be a complex of closed and open circuits that are carrying out two fundamentally different problems: a locally closed circuits are meeting local demands by supplying of locally produced goods that is providing self-reproduction of the local economy; open (or transit) circuits are providing export-import flows. The generation mechanism of «internal» money in closed circuits of commodity-production network is presented. Obtained theoretical results are illustrated by calculations of closed and open circuit flows in the municipality economy model. The mathematical model represents the mutual payments of population and companies in matrix form. It is found that the volume of turnover in closed circuits is about 28.5 % of total municipality economy model turnover and can be assured by «internal» non-inflationary money. The remaining 71.5 % of total turnover are corresponding to the flows in open circuits of the network providing export and import activity. It is summarized that the priority of innovative economy projects should be given to domestic consumption rather than exports.

Commodity and Financial Networks in Regional Economics

The article discusses the relationship between commodity-production and financial network structures in the regional economy as dual conjugate systems. Material flows (raw materials, goods and so on) circulate in the commodity network as shown by Leontiev’s input-output balance model. Nonmaterial flows of property rights, money, and so on circulate in the financial network and reflect the movement of material objects in commodity networks. A network structure comprises closed and open circuits, which have fundamentally different characteristics: locally closed circuits meet local demand by supplying locally produced goods, thus ensuring self-reproduction of the local economy; open (or transit) circuits provide export-import flows. The article describes the mechanism of ‘internal’ money generation in closed circuits of commodity-production networks. The results of the theoretical study are illustrated by the calculations of closed and open circuit flows in the municipal economy model. Mutual settlements between the population and manufacturing enterprises are given in matrix form. It was found that the volume of the turnover in closed circuits of the municipal economic network model is about 28.5 % of the total turnover and can be provided by ‘internal’ non-inflationary money. The remaining 71.5 % of the total turnover correspond to the flows in the network’s open circuits providing export and import. The conclusion is made that in the innovation-driven economy, main attention should be given to the projects oriented towards domestic consumption rather than export supplies. The economy is based on internal production cycles in closed circuits. Thus, it is necessary to find the chains in the inter-industrial and inter-production relations which could become the basis of the production cycle. Money investments will complete such commodity chains and ‘launch’ the production cycle.

Learning Network Structure of Financial Institutions from CDS Data

Default contagion is a concern especially after witnessing the financial crisis. How the financial institutions are connected is crucial to how likely they will default together. However, the information on financial network structure is not available. This paper builds a probabilistic graphical model relating the network structure to observable prices. The financial network structure can be learned using CDS data. Conditional default probability and expected conditional loss can be calculated accordingly. As the information is extracted from the CDS price, all quantities are under the risk-neutral measure, and thus account for the market fear that can trigger bank run. This knowledge can be used to gauge systemic risk and inform policy decisions.

Unveiling the connectivity structure of financial networks via high-frequency analysis

The paper deals with the problem of reconstructing the internal link structure of a network of agents subject to mutual dependencies. We show that standard multivariate approaches based on a correlation analysis are not well suited to detect mutual influences and dependencies, especially in the presence of delayed or propagative relations and when the sampling rate is sufficiently high to capture them. In particular, we develop and apply a metric based on the coherence function to take into account these dynamical phenomena. The effectiveness of the proposed approach is illustrated through numerical examples and through the analysis of a real complex networked system, i.e. a set of 100 high volume stocks of the New York Stock Exchange, observed during March 2008 and sampled at high frequency.