Financial network

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A financial network is a concept describing any collection of financial entities (such as payment card companies, firms, banks and financial transaction processing) and the links between them, ideally through direct transactions or the ability to mediate a transaction. [1] A common example of a financial network link is security holdings (e.g. stock of publicly traded companies), 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 (i.e. stock or bond ownership). [2]

Contents

History

The concept and use of financial networks has emerged in response to the observation that modern financial systems exhibit a high degree of interdependence. [2] Globalization has magnified the level of financial interdependence across many kinds of organizations. Shares, assets, and financial relationships are held and engaged in at a greater degree over time. The trend is a topic of major interest in the financial sector, particularly due to its implications on financial crises. [3]

The Crises have played a major role in developing the understanding of financial networks. In 1998, the crash of Long-Term Capital Management (LTCM) exposed their underlying importance. [4] In particular, the LTCM case highlighted the hidden correlations inherent in financial networks. In the case of LTCM, financial correlations were much higher than expected between Japanese bonds and Russian bonds. LTCM took on a significant amount of risk (at one point leveraged 25:1) to trade on this relationship, while underestimating these correlations. The 1997 Asian financial crisis and the subsequent 1998 Russian financial crisis lead to a divergence of European, Japanese and U.S. bonds, causing the collapse of LTCM. The ensuing crisis in the market proved the impact that financial networks can have. [5] Similarly, after the 2008 financial crisis, many economists have come around to the view the very networked architecture of the financial system plays a central role in shaping systemic risk. In fact, many of the ensuing policy actions have been motivated by these insights. [6]

Applications

As a result of these insights, network science concepts have been cross-applied to the finance field. As of 2008, the literature in the field was rather nascent. [2] Broadly speaking, data on interbank relationships and transactions can be hard to come by. [7] This can limit the number of applicable use cases. Nevertheless, there are some major areas of interest and applications for the study of financial networks. Some of these are financial contagion and system risk, [8] the formation of interbank markets, [9] and characterization of current financial systems. [10] Other applications of financial networks are stock correlation networks, interbank networks, and agent-based models. Some agent based finance models which utilize a limit order book are instances of financial networks, where traders are connected to at least one exchange, and the exchange mediates transactions between traders. [11] [12] Another area of study is cascades in financial networks, which helps scientists and policymakers determine how to mitigate financial crises. [13]

The network science concepts that have been applied to financial networks are numerous and varied. Stiglitz, et.al. applied the concept and math behind multilayer networks to assess the cost of increased complexity in financial networks. [14] Battiston, et al. utilized centrality to develop the DebtRank algorithm, a means to assess the systemic risk certain institutions can pose based on their connectedness and debt levels. [8] Others have focused extensively on developing benchmark models for the structure of financial networks; some take the economics-driven approach by forecasting nodes as rational actors in a system, while others take a more statistical physics approach, [2] an approach justified by the necessity of reconstructing the network because the information available is often incomplete. [15]

See also

Systemic risk

Cascading failure

Related Research Articles

In economics and finance, arbitrage is the practice of taking advantage of a difference in prices in two or more markets – striking a combination of matching deals to capitalise on the difference, the profit being the difference between the market prices at which the unit is traded. When used by academics, an arbitrage is a transaction that involves no negative cash flow at any probabilistic or temporal state and a positive cash flow in at least one state; in simple terms, it is the possibility of a risk-free profit after transaction costs. For example, an arbitrage opportunity is present when there is the possibility to instantaneously buy something for a low price and sell it for a higher price.

Long-Term Capital Management L.P. (LTCM) was a highly leveraged hedge fund. In 1998, it received a $3.6 billion bailout from a group of 14 banks, in a deal brokered and put together by the Federal Reserve Bank of New York.

A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations, an ecosystem, a living cell, and ultimately the entire universe.

<span class="mw-page-title-main">Financial market</span> Generic term for all markets in which trading takes place with capital

A financial market is a market in which people trade financial securities and derivatives at low transaction costs. Some of the securities include stocks and bonds, raw materials and precious metals, which are known in the financial markets as commodities.

<span class="mw-page-title-main">Cascading failure</span> Systemic risk of failure

A cascading failure is a failure in a system of interconnected parts in which the failure of one or few parts leads to the failure of other parts, growing progressively as a result of positive feedback. This can occur when a single part fails, increasing the probability that other portions of the system fail. Such a failure may happen in many types of systems, including power transmission, computer networking, finance, transportation systems, organisms, the human body, and ecosystems.

<span class="mw-page-title-main">Lender of last resort</span> Government guarantee to provide liquidity to financial institutions

In public finance, 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 when other facilities or such sources have been exhausted. It is, in effect, a government guarantee to provide 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.

<span class="mw-page-title-main">Financial contagion</span> Scenario in which financial shocks spread to other financial sectors

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 financial crisis is any of a broad variety of situations in which some financial assets suddenly lose a large part of their nominal value. In the 19th and early 20th centuries, many financial crises were associated with banking panics, and many recessions coincided with these panics. Other situations that are often called financial crises include stock market crashes and the bursting of other financial bubbles, currency crises, and sovereign defaults. Financial crises directly result in a loss of paper wealth but do not necessarily result in significant changes in the real economy.

In complex network theory, the fitness model is a model of the evolution of a network: how the links between nodes change over time depends on the fitness of nodes. Fitter nodes attract more links at the expense of less fit nodes.

<span class="mw-page-title-main">Guido Caldarelli</span> Italian scientist (born 1967)

Guido Caldarelli is an Italian physicist and full professor in Theoretical Physics at Ca' Foscari University of Venice.

In financial economics, a liquidity crisis is an acute shortage of liquidity. Liquidity may refer to market liquidity, funding liquidity, or accounting liquidity. Additionally, some economists define a market to be liquid if it can absorb "liquidity trades" without large changes in price. This shortage of liquidity could reflect a fall in asset prices below their long run fundamental price, deterioration in external financing conditions, reduction in the number of market participants, or simply difficulty in trading assets.

The Subprime mortgage crisis solutions debate discusses various actions and proposals by economists, government officials, journalists, and business leaders to address the subprime mortgage crisis and broader 2007–2008 financial crisis.

A stock correlation network is a type of financial network based on stock price correlation used for observing, analyzing and predicting the stock market dynamics.

<span class="mw-page-title-main">Stephany Griffith-Jones</span>

Stephany Griffith-Jones is an economist specializing in international finance and development. Her expertise lies in the reform of the international financial system, particularly in financial regulation, global governance, and international capital flows. Currently, she serves as a member of the Governor Board at the Central Bank of Chile. She has held various positions throughout her career, including financial markets director at the Initiative for Policy Dialogue based at Columbia University, associate fellow at the Overseas Development Institute, and professorial fellow at the Institute of Development Studies at Sussex University.

<span class="mw-page-title-main">Dependency network</span>

The dependency network approach provides a system level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the network's nodes, and provides an important step towards inference of causal activity relations between the network nodes. This methodology has originally been introduced for the study of financial data, it has been extended and applied to other systems, such as the immune system, and semantic networks.

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.

<span class="mw-page-title-main">Bianconi–Barabási model</span>

The Bianconi–Barabási model is a model in network science that explains the growth of complex evolving networks. This model can explain that nodes with different characteristics acquire links at different rates. It predicts that a node's growth depends on its fitness and can calculate the degree distribution. The Bianconi–Barabási model is named after its inventors Ginestra Bianconi and Albert-László Barabási. This model is a variant of the Barabási–Albert model. The model can be mapped to a Bose gas and this mapping can predict a topological phase transition between a "rich-get-richer" phase and a "winner-takes-all" phase.

In network science, the network entropy is a disorder measure derived from information theory to describe the level of randomness and the amount of information encoded in a graph. It is a relevant metric to quantitatively characterize real complex networks and can also be used to quantify network complexity

References

  1. ↑ Nagurney, A.; Ke, K. (2001). "Financial networks with intermediation". Quantitative Finance. 1 (4): 441–451. doi:10.1088/1469-7688/1/4/304. S2CID   154276256.
  2. 1 2 3 4 Allen, Franklin (2008). "Networks in Finance" (PDF). Network-based Strategies and Competencies.
  3. ↑ Elliott, Golub, and Jackson (2014). "Financial Networks and Contagion" (PDF). American Economic Review. 104 (10): 3115–53. doi:10.1257/aer.104.10.3115.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  4. ↑ Financial Networks - Statics and Dynamics | Anna Nagurney | Springer. Advances in Spatial Science. Springer. 1997. doi:10.1007/978-3-642-59066-5. ISBN   978-3-642-63835-0.
  5. ↑ Lowenstein, Roger. When Genius Failed: The Rise and Fall of Long-Term Capital Management. New York: Random House, 2000. Print.
  6. ↑ Acemoglu, Daron (2015). "Systemic Risk and Stability in Financial Networks". American Economic Review. 105 (2): 564–608. doi:10.1257/aer.20130456. hdl: 1721.1/100979 . S2CID   7447939.
  7. ↑ "Research - Zhenyu Cui". sites.google.com. Retrieved 2017-12-23.
  8. 1 2 Battiston, Stefano; Puliga, Michelangelo; Kaushik, Rahul; Tasca, Paolo; Caldarelli, Guido (2012-08-02). "DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk". Scientific Reports. 2 (1): 541. Bibcode:2012NatSR...2E.541B. doi:10.1038/srep00541. ISSN   2045-2322. PMC   3412322 . PMID   22870377.
  9. ↑ Babus, Ana (2016). "The Formation of Financial Networks" (PDF). The RAND Journal of Economics. 47 (2): 239–272. doi:10.1111/1756-2171.12126.
  10. ↑ Diebold, Francis (2016). "Estimating global bank network connectedness" (PDF). Journal of Applied Econometrics.
  11. ↑ Bookstaber et al. (2014). An Agent-based Model for Financial Vulnerability. OFR Working Paper Series. Available at: http://www.treasury.gov/initiatives/ofr/research/Documents/OFRwp2014-05_BookstaberPaddrikTivnan_Agent-basedModelforFinancialVulnerability_revised.pdf
  12. ↑ Qiu, Tian; Zheng, Bo; Chen, Guang (2010). "Financial networks with static and dynamic thresholds". New Journal of Physics. 12 (4): 043057. arXiv: 1002.3432 . Bibcode:2010NJPh...12d3057Q. doi:10.1088/1367-2630/12/4/043057. S2CID   17988309.
  13. ↑ Minoiu, Camelia Minoiu. "IMF Survey : Financial Networks Key to Understanding Systemic Risk." IMF Survey : Financial Networks Key to Understanding Systemic Risk. N.p., 28 May 2014. Web. 21 Nov. 2014.
  14. ↑ Battiston, Stefano; Caldarelli, Guido; May, Robert; Roukny, Tarik; Stiglitz, Joseph (2016). "The price of complexity in financial networks". PNAS. 113 (36): 10031–10036. Bibcode:2016PNAS..11310031B. doi: 10.1073/pnas.1521573113 . PMC   5018742 . PMID   27555583.
  15. ↑ Cimini, Giulio; Squartini, Tiziano; Saracco, Fabio; Garlaschelli, Diego; Gabrielli, Andrea; Caldarelli, Guido (2019). "The Statistical Physics od Real World Networks". Nature Reviews Physics. 1 (1): 52–70. arXiv: 1810.05095 . Bibcode:2019NatRP...1...58C. doi:10.1038/s42254-018-0002-6. S2CID   52963395.
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