---
# System prepended metadata

title: Systemic risk

---

# Systemic risk

The analysis for [late payment](/iIVkdpz_S76a_6FexHZWAw) and [default](/nO2IbqcMTbiNSY0AG0PCnA) worked on the assumption that within the club the value of one MCU is equal to the value of one fiat unit. Any possible event that leads to loss of credit value relative to fiat currency is considered a source of systemic risk.

Systemic risk is harder to measure than such 'normal' risks (although it could plausibly be estimated with sufficient historical data on systemic risk events e.g. collapse from a representative sample of similar clubs), so the potential for reduction of normal risk at the expense of hidden systemic risk in particular should be carefully considered.

## Use of positive clearing limits to hide risk

Large enough positive [clearing limits](/AYLZ5n-WTG6eiRVSlh0qOw) could be used to achieve an apparent total elimination the 'normal' risks of [late payment](/iIVkdpz_S76a_6FexHZWAw) and [default](/nO2IbqcMTbiNSY0AG0PCnA) for all members, but this would merely have the effect of transforming them into systemic risk arising from the potential for a loss of confidence in other members' ability to settle in fiat or back the their credit with goods and services if required. 

## Member 'extinction'

Percolation theory


## Club collapse

# Resources
* [Do Bankers Need to Be Put Into Little Boxes?](https://economistsview.typepad.com/economistsview/2011/06/do-bankers-need-to-be-put-into-little-boxes.html) - compartmentalisation of the finance sector - '... [in] an interconnected network that can spread problems from institution to institution, there are two possible scenarios. First, think of a drop of poison in the ocean. The ocean is so big that even a powerful poison can be neutralized as it spreads... In this case, you do not want to have the network compartmentalized... This is much like traditional financial risk sharing where large individual losses are spread throughout the system... But now think of a poison that acts more like an infection. As it spreads it does not become less toxic, it continues to be lethal to anyone who comes in contact with it. In this case, you want to break the network connections.'
* [Bank Networks: Contagion, Systemic Risk andPrudential Policy](https://aldasoro.me/wp-content/uploads/2015/12/Aldasoro_DelliGatti_Faia_7_12_2015.pdf) - '... a network model of the interbank market in which optimizing risk averse bankslend to each other and invest in non-liquid assets... Contagion occurs through liquidity hoarding,interbank interlinkages and fire sale externalities. The resulting network configuration exhibits a core-periphery structure, dis-assortative behavior and low density. Within this frameworkwe analyze the effects of prudential policies on the stability/efficiency trade-off.  Liquidity requirements unequivocally decrease systemic risk but at the cost of lower efficiency... Equity requirements tend to reduce risk without reducing significantly overall investment.'
* [Systemic risk in banking ecosystems](https://www.nature.com/articles/nature09659) - 'In the run-up to the recent financial crisis, an increasingly elaborate set of financial instruments emerged, intended to optimize returns to individual institutions with seemingly minimal risk. Essentially no attention was given to their possible effects on the stability of the system as a whole. Drawing analogies with the dynamics of ecological food webs and with networks within which infectious diseases spread, we explore the interplay between complexity and stability in deliberately simplified models of financial networks. We suggest some policy lessons that can be drawn from such models, with the explicit aim of minimizing systemic risk.'
* [The Network of Global Corporate Control](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0025995) - 'The structure of the control network of transnational corporations affects global market competition and financial stability... We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy makers.'
* [The Global Financial Markets: An Ultra-Large-Scale Systems Perspective](https://link.springer.com/chapter/10.1007/978-3-642-34059-8_2) - '...the world’s financial markets have become a globally interconnected complex adaptive ultra-large-scale socio-technical system-of-systems, and that this has important consequences for how the financial markets should be engineered and managed in future. Major failures... may become more commonplace in future, unless lessons are learned from other fields where complex adaptive socio-technical systems-of-systems have to be engineered for high-integrity, safety-critical applications. In this document we review the literature on failures in risky technology and high-integrity approaches to safety-critical SoS engineering. We conclude with an argument that, in the specific case of the global financial markets, there is an urgent need to develop major national strategic modeling and predictive simulation capabilities, comparable to national-scale meteorological monitoring and modeling capabilities. The intent here is... to provide test-rigs for principled evaluation of systemic risk, estimating probability density functions over spaces of possible outcomes, and thereby identifying potentially high-consequence failure modes in the simulations, before they occur in real life, by which time it is typically too late.'
* [Elimination of systemic risk in financial networks by means of a systemic risk transaction tax](https://arxiv.org/abs/1401.8026) - '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 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 (CRISIS macro-financial model) we demonstrate that the proposed "Systemic Risk Tax" (SRT) leads to a self-organised 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.'
* [Foreseeing the next financial crisis... ](https://physicsoffinance.blogspot.com/2013/11/foreseeing-next-financial-crisis.html) - list of links to research papers on systemic risk.
* [Let there be light](https://physicsoffinance.blogspot.com/2013/02/let-there-be-light.html) - '...an idea for improving the function of the interbank lending market... a complete transformation of banking transparency. It shows how transparency may be the best route to achieving overall banking stability and efficiency.' (Original papers on DebtRank [here](https://arxiv.org/abs/1301.6115) and [here](https://www.nature.com/articles/srep00541)).
* [Stop banking abuse with radical information transparency](https://physicsoffinance.blogspot.com/2014/03/stop-banking-abuse-with-radical.html)- 'an important developing body of work showing how algorithms -- closely linked to Google's PageRank algorithm -- could be used to greatly increase transparency surrounding issues of systemic risk. The systemic risk linked to any one bank or even to any single financial transaction could be made apparent to everyone; call it "radical transparency." Coupled with other mechanisms, such transparency could provide a route to making the system safer on is own, through normal economic self organization. The idea, in essence, is to use computation to give everyone in the market the information they need to make better choices.'
* [Sharing risk can increase risk](https://physicsoffinance.blogspot.com/2011/10/sharing-risk-can-increase-risk.html) - '...more risk sharing between institutions can, in some cases, lead to greater systemic risk... credit networks can both help institutions to pool resources to achieve things they couldn't on their own, and to diversify against the risks they face. At the same time, the linking together of institutions by contracts implies a greater chance for the propagation of financial stress from one place to another.'
* [Liaisons Dangereuses: Increasing Connectivity, Risk Sharing, and Systemic Risk](https://www.nber.org/papers/w15611) - '...the evolution over time of a network of credit relations among financial agents as a system of coupled stochastic processes. Each process describes the dynamics of individual financial robustness, while the coupling results from a network of liabilities among agents. The average level of risk diversification of the agents coincides with the density of links in the network. In addition to a process of diffusion of financial distress, we also consider a discrete process of default cascade, due to the re-evaluation of agents' assets. In this framework we investigate the probability of individual defaults as well as the probability of systemic default as a function of the network density. While it is usually thought that diversification of risk always leads to a more stable financial system, in our model a tension emerges between individual risk and systemic risk. As the number of counterparties in the credit network increases beyond a certain value, the default probability, both individual and systemic, starts to increase. This tension originates from the fact that agents are subject to a financial accelerator mechanism. In other words, individual financial fragility feeding back on itself may amplify the effect of an initial shock and lead to a full fledged systemic crisis. The results offer a simple possible explanation for the endogenous emergence of systemic risk in a credit network.'
* [Eroding market stability by proliferation of financial instruments](https://arxiv.org/abs/0910.0064) - 'We contrast Arbitrage Pricing Theory (APT), the theoretical basis for the development of financial instruments, with a dynamical picture of an interacting market, in a simple setting. The proliferation of financial instruments apparently provides more means for risk diversification, making the market more efficient and complete. In the simple market of interacting traders discussed here, the proliferation of financial instruments erodes systemic stability and it drives the market to a critical state characterized by large susceptibility, strong fluctuations and enhanced correlations among risks. This suggests that the hypothesis of APT may not be compatible with a stable market dynamics. In this perspective, market stability acquires the properties of a common good, which suggests that appropriate measures should be introduced in derivative markets, to preserve stability.'
* [Forecasting Financial Crises](https://forecastingcrises.wordpress.com/) - the blog of the FOC project.
* [Macroeconomics after the crisis: time to deal with the pretense-of-knowledge syndrome](https://www.nber.org/system/files/working_papers/w16429/w16429.pdf) - 'banks normally collect basic information about their direct trading partners, which serves to assure them of the soundness of these relationships. However, when acute financial distress emerges in parts of the financial network, it is not enough to be informed about these direct trading partners, but it also becomes important for the banks to learn about the health of the partners of their trading partners to assess the chances of an indirect hit. As conditions continue to deteriorate, banks must learn about the health of the trading partners of the trading partners of their trading partners, and so on. At some point, the cost of information gathering becomes too large and the banks, now facing enormous uncertainty, choose to withdraw from loan commitments and illiquid positions.'
* [Effective measurement of network
vulnerability under random and intentional
attacks](https://www.researchgate.net/publication/220121243_Effective_measurement_of_network_vulnerability_under_random_and_intentional_attacks) - 'The study of the security and stability of complex networks plays a central role in reducing the risk and consequences of attacks or disfunctions of any type. The concept of vulnerability helps to measure the response of complex networks subjected to attacks on vertices and edges and it allows to spot the critical component of a network in order to improve its security. We introduce an accurate and computable
deﬁnition of network vulnerability which is directly connected with its topology and we analyze its basic properties. We discuss the relationship of the vulnerability with other parameters of the network and we illustrate this with some examples.'
* [Collective Risk Management in a Flight to Quality Episode](http://economics.mit.edu/files/3679) - 'Severe flight to quality episodes involve uncertainty about the environment, not only risk about asset payoffs. The uncertainty is triggered by unusual events and untested financial innovations that lead agents to question their worldview. We present a model of crises and central bank policy that incorporates Knightian uncertainty. The model explains crisis  regularities such as market-wide capital  immobility, agents’ disengagement from risk, and liquidity hoarding. We identify a social cost of these behaviors, and a benefit of a lender of last resort facility. The benefit is particularly high because public and private insurance are  complements during uncertainty-driven crises.'
* [Complexity and financial panics](https://www.nber.org/system/files/working_papers/w14997/w14997.pdf) - 'During extreme financial crises, all of a sudden, the financial world that was once rife with profit opportunities for financial institutions (banks, for short) becomes exceedingly complex. Confusion and uncertainty follow, ravaging financial markets and triggering massive flight-to-quality episodes. In this paper we propose a model of this phenomenon. In our model, banks normally collect information about their trading partners which assures them of the soundness of these relationships. However, when acute financial distress emerges in parts of the financial network, it is not enough to be informed about these partners, as it also becomes important to learn about the health of their trading partners. As conditions continue to deteriorate, banks must learn about the health of the trading partners of the trading partners of the trading partners, and so on. At some point, the cost of information gathering becomes too unmanageable for banks, uncertainty spikes, and they have no option but to withdraw from loan commitments and illiquid positions. A flight-to-quality ensues, and the financial crisis spreads.'
* [Fire Sales in a Model of Complexity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1496592) - 'In this paper we present a model of fire sales and market breakdowns, and of the financial amplification mechanism that follows from them. The distinctive feature of our model is the central role played by endogenous uncertainty. As conditions deteriorate, more “banks” within the financial network become distressed, which increases each (non-distressed) bank’s likelihood of being hit by an indirect shock. As this happens, banks face an increasingly complex environment since they need to understand more and more interlinkages in making their financial decisions. Uncertainty comes as a by-product of this complexity, and makes relatively healthy banks, and hence potential asset buyers, reluctant to buy. The liquidity of the market quickly vanishes and a financial crisis ensues. The model features a novel complexity externality which provides a rationale for various government policies commonly used during financial crises, including bailouts and asset price supports.'
* [R. Caballero's research profile](https://www.researchgate.net/scientific-contributions/Ricardo-J-Caballero-7867868).
* [Cascades in Networks and Aggregate Volatility](http://economics.mit.edu/files/6147) - 'We provide a general framework for the study of cascade effects created by interconnections between sectors, firms or financial institutions.  Focusing on a multi-sector economy linked through a supply network, we show how structural properties of the supply network determine both whether aggregate volatility disappears as the number of sectors increases (i.e., whether the law of large numbers holds) and when it does, the rate at which this happens. Our main results characterize the relationship between first-order interconnections (captured by the weighted degree sequence in the graph induced by the input-output relations) and aggregate volatility, and more importantly, the relationship between higher-order interconnections and aggregate volatility. These higher-order interconnections capture the cascade  effects, whereby low productivity or the failure  of a set of suppliers propagates through the rest of the economy as their downstream sectors/firms also suffer and transmit the negative shock to their downstream sectors/firms. We also link the probabilities of tail events (large negative deviations of aggregate output from its mean) to sector-specific volatility and to the structural properties of the supply network.'
* [Self-Organized Criticality and Economic Fluctuations](https://www.jstor.org/stable/2117870?seq=1#metadata_info_tab_contents)
* [Complexity and Empirical Economics](https://academic.oup.com/ej/article/115/504/F225/5085667?login=true)
* [Systemic Risk and Stability in Financial Networks](https://www.aeaweb.org/articles?id=10.1257/aer.20130456) - '... the extent of financial contagion exhibits a form of phase transition: as long as the magnitude of negative shocks affecting financial institutions are sufficiently small, a more densely connected financial network (corresponding to a more diversified pattern of interbank liabilities) enhances financial stability. However, beyond a certain point, dense interconnections serve as a mechanism for the propagation of shocks, leading to a more fragile financial system. Our results thus highlight that the same factors that contribute to resilience under certain conditions may function as significant sources of systemic risk under others.'
* [Measuring Model Risk in Financial Risk Management and Pricing](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3113139) - 'Risk measurement and pricing of financial positions are based on modeling assumptions, which are common assumptions on the probability distribution of the position's outcomes. We associate a model with a probability measure and investigate model risk by considering a model space.'
* [How likely is contagion in financial networks?](https://www.sciencedirect.com/science/article/abs/pii/S0378426614000600?via%3Dihub) - 'Interconnections among financial institutions create potential channels for contagion and amplification of shocks to the financial system. We estimate the extent to which interconnections increase expected losses and defaults under a wide range of shock distributions. In contrast to most work on financial networks, we assume only minimal information about network structure and rely instead on information about the individual institutions that are the nodes of the network. The key node-level quantities are asset size, leverage, and a financial connectivity measure given by the fraction of a financial institution’s liabilities held by other financial institutions. We combine these measures to derive explicit bounds on the potential magnitude of network effects on contagion and loss amplification. Spillover effects are most significant when node sizes are heterogeneous and the originating node is highly leveraged and has high financial connectivity. Our results also highlight the importance of mechanisms that go beyond simple spillover effects to magnify shocks; these include bankruptcy costs, and mark-to-market losses resulting from credit quality deterioration or a loss of confidence. We illustrate the results with data on the European banking system.'
* [Catastrophic cascade of failures in interdependent networks](https://www.nature.com/articles/nature08932/) - 'Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network. Modern systems are coupled together and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures (‘concurrent malfunction’) is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations20. Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.'

## Resources with commentary
* [Pathways towards instability in financial networks](https://workflowy.com/#/dd4273f5fe5d)