The "too connected to fail" (TCTF) concept refers to a financial institution which is so connected to other institutions that its failure would probably lead to a huge turnover in the whole system.Contrary to the "too big to fail" theory, this approach takes into consideration the highly connected network feature of the financial system rather than the absolute size of one particular institution.
The 2007/2008 financial crisis highlighted that a small turmoil can cause a big fallback in the financial system – mainly because financial institutions form a highly interconnected network. From a network science point of view, this means that some nodes (institutions) have very high degree, i.e. they are linked to many other nodes. As a consequence, they play a central role in the system, which can be highly important in the case of disturbances.Recognition of this effect led to the revival of macroprudential regulation.
Determining which institute is TCTF is not as straightforward as in the case of the "too big to fail" theory. In this case, one doesn't have easily measurable metrics like assets’ value or the volume of financial services. However, there are some approaches trying to establish a clear method in order to identify the key institutions in the network.
Nacaskulposits that a financial institution is systemically important if it is highly connected (e.g. via interbank lending market/money market channel) to systemically important banks. Those, in turn, are systemically important if they are highly connected to systemically important banks, and so on. The recursive definition is equivalent to performing eigendecomposition of a matrix of connectivity weights and assigning systemic importance in proportion to the values of the principal eigenvector. The "entropic" factor correction is introduced therein to correct for the possibility that performing eigendecomposition on weighted connectivity matrices may occasionally yield "degenerate" systemic importance scores (all financial institutions identical in terms of systemic importance). Nacaskul & Sabborriboon then extends the Systemic Importance Analysis (SIA) above, which focuses on systemic leverage each individual financial institution exerts on the overall system, to Systemic Vulnerability Analysis (SVA), whereby the overall system is assessed as to how vulnerable it is to disproportionate systemic leverages exerted by individual financial institutions, and applied the methodology to Thai interbank money-market funding matrix.
If one defines the links connecting the different nodes in the network, then the TCTF feature of a certain node can be examined using network science methodology. The most simple way of thinking about the role of an institution is the number of connections it has, which is called the degree of the node. Depending on the type of the network, one can define in and out-degree. Knowing the key players one can test the risk involved in the network by simulating targeted attacks (shocks).
One example for this method is the paper of León et al.,which analyzed the systemic risk within Colombia’s financial market. They defined an institution's in-degree as its share in total traded value, and the out-degree as its share of total number of connections based on transactions in the paying system. Using these metrics they constructed an index of centrality that let them identify the key institutions in the system, and made it possible to test the network's resistance to shocks.
Another way to measure the TCTF feature of an institution is based on the concept of feedback centrality. One example for this is the DebtRank introduced in the paper of Battiston et al.The authors defined financial institutions as nodes and directed edges as lending relations weighted by the amount of outstanding debt. Then, they computed the DebtRank for every node, which measures that in the case of the distress of the particular node what fraction of the total economic value is potentially affected. By doing this, they identified the key financial institutions in the US between 2008 and 2010.
A cascading failure is a process in a system of interconnected parts in which the failure of one or few parts can trigger the failure of other parts and so on. Such a failure may happen in many types of systems, including power transmission, computer networking, finance, transportation systems, organisms, the human body, and ecosystems.
Network theory is the study of graphs as a representation of either symmetric relations or asymmetric relations between discrete objects. In computer science and network science, network theory is a part of graph theory: a network can be defined as a graph in which nodes and/or edges have attributes.
A lender of last resort (LOLR) is the institution in a financial system that acts as the provider of liquidity to a financial institution which finds itself unable to obtain sufficient liquidity in the interbank lending market and other facilities or sources have been exhausted. It is, in effect, a government guarantee of liquidity to financial institutions. Since the beginning of the 20th century, most central banks have been providers of lender of last resort facilities, and their functions usually also include ensuring liquidity in the financial market in general.
In finance, systemic risk is the risk of collapse of an entire financial system or entire market, as opposed to the risk associated with any one individual entity, group or component of a system, that can be contained therein without harming the entire system. It can be defined as "financial system instability, potentially catastrophic, caused or exacerbated by idiosyncratic events or conditions in financial intermediaries". It refers to the risks imposed by interlinkages and interdependencies in a system or market, where the failure of a single entity or cluster of entities can cause a cascading failure, which could potentially bankrupt or bring down the entire system or market. It is also sometimes erroneously referred to as "systematic risk".
Liquidity risk is a financial risk that for a certain period of time a given financial asset, security or commodity cannot be traded quickly enough in the market without impacting the market price.
In graph theory and network analysis, indicators of centrality identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, super-spreaders of disease, and brain networks. Centrality concepts were first developed in social network analysis, and many of the terms used to measure centrality reflect their sociological origin. They should not be confused with node influence metrics, which seek to quantify the influence of every node in the network.
Financial contagion refers to "the spread of market disturbances – mostly on the downside – from one country to the other, a process observed through co-movements in exchange rates, stock prices, sovereign spreads, and capital flows". Financial contagion can be a potential risk for countries who are trying to integrate their financial system with international financial markets and institutions. It helps explain an economic crisis extending across neighboring countries, or even regions.
A payment system is any system used to settle financial transactions through the transfer of monetary value. This includes the institutions, instruments, people, rules, procedures, standards, and technologies that make its exchange possible. A common type of payment system is called an operational network that links bank accounts and provides for monetary exchange using bank deposits. Some payment systems also include credit mechanisms, which are essentially a different aspect of payment.
Financial risk is any of various types of risk associated with financing, including financial transactions that include company loans in risk of default. Often it is understood to include only downside risk, meaning the potential for financial loss and uncertainty about its extent.
In graph theory, eigenvector centrality is a measure of the influence of a node in a network. Relative scores are assigned to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes. A high eigenvector score means that a node is connected to many nodes who themselves have high scores.
The "too big to fail" (TBTF) theory asserts that certain corporations, particularly financial institutions, are so large and so interconnected that their failure would be disastrous to the greater economic system, and that they therefore must be supported by governments when they face potential failure. The colloquial term "too big to fail" was popularized by U.S. Congressman Stewart McKinney in a 1984 Congressional hearing, discussing the Federal Deposit Insurance Corporation's intervention with Continental Illinois. The term had previously been used occasionally in the press, and similar thinking had motivated earlier bank bailouts.
A stress test, in financial terminology, is an analysis or simulation designed to determine the ability of a given financial instrument or financial institution to deal with an economic crisis. Instead of doing financial projection on a "best estimate" basis, a company or its regulators may do stress testing where they look at how robust a financial instrument is in certain crashes, a form of scenario analysis. They may test the instrument under, for example, the following stresses:
In graph theory and social network analysis, alpha centrality is an alternative name for Katz centrality. It is a measure of centrality of nodes within a graph. It is an adaptation of eigenvector centrality with the addition that nodes are imbued with importance from external sources.
A network is an abstract structure capturing only the basics of connection patterns and little else. Because it is a generalized pattern, tools developed for analyzing, modeling and understanding networks can theoretically be implemented across disciplines. As long as a system can be represented by a network, there is an extensive set of tools – mathematical, computational, and statistical – that are well-developed and if understood can be applied to the analysis of the system of interest.
Banking in Kosova has been developing significantly since the country declared its independence in 2008. Banking in Kosovo is made up of a network centered on the Central Bank of Kosovo (CBK) with the nodes being commercial banks and other micro financial institutions.
Banking in Albania, in its present form dating from 1992, consists of the nation's central bank - the Bank of Albania - and an expanding network of secondary banks. The Bank of Albania has the task of supervising the financial system, which currently contains 16 privately owned banks and many other financial institutions.
In network theory, multidimensional networks, a special type of multilayer network, are networks with multiple kinds of relations. Increasingly sophisticated attempts to model real-world systems as multidimensional networks have yielded valuable insight in the fields of social network analysis, economics, urban and international transport, ecology, psychology, medicine, biology, commerce, climatology, physics, computational neuroscience, operations management, infrastructures and finance.
A financial network is a concept describing any collection of financial entities and the links between them, ideally through direct transactions or the ability to mediate a transaction. A common example of a financial network link is security holdings, where a firm’s ownership of stock would represent a link between the stock and the firm. In network science terms, financial networks are composed of financial nodes, where nodes represent financial institutions or participants, and of edges, where edges represent formal or informal relationships between nodes.
Rama Cont is the Professor of Mathematical Finance at the University of Oxford. He is known for contributions to probability theory, stochastic analysis and mathematical modelling in finance, in particular mathematical models of systemic risk. He was awarded the Louis Bachelier Prize by the French Academy of Sciences in 2010.