We develop a dynamic model of information transmission and aggregation in social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity, in which agents’ opinions are widely dispersed.

This study addresses a critical regulatory shortfall by developing a platform to extend stress testing from a microprudential approach to a dynamic, macroprudential approach. This paper describes the ensuing agent-based model for analyzing the vulnerability of the financial system to asset- and funding-based fire sales. The model captures the dynamic interactions of agents in the financial system extending from the suppliers of funding through the intermediation and transformation functions of the bank/dealers to the financial institutions that use the funds to trade in the asset markets. The model replicates the key finding that it is the reaction to initial losses, rather than the losses themselves, that determine the extent of a crisis. By building on a detailed mapping of the transformations and dynamics of the financial system, the agent-based model provides an avenue toward risk management that can illuminate the pathways for the propagation of key crisis dynamics such as fire sales and funding runs.

Markets coordinate the flow of information in the economy, aggregating it through the price mechanism. We develop a dynamic model of information transmission and aggregation in financial and other social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity in which agents’ opinions are widely dispersed. We add varying degrees of homophily through a model parameter called tribal preference and find that crash frequency is decreasing in the degree of homophily. Our results suggest that the information aggregation function of markets can fail solely because of the dynamics of information flows, irrespective of shocks or news.

This article describes the agent-based approach to modeling financial crises. It focuses on the interactions of agents and on how these interactions feed back to change the financial environment. It explains how these models embody the contagion and cascades that occur owing to the financial leverage and market concentration of the agents and the liquidity of the markets. This article also compares agent-based models to the standard economic approach to crises and shows the ways in which agent-based models overcome limitations of economic models when dealing with financial crises. In particular, this article demonstrates how agent-based models replace homogeneous, representative agents with heterogeneous agents and optimization with heuristics, and how such models move away from a focus on equilibrium, allowing non-ergodic dynamics that are manifest during financial crises to emerge.

This brief introduces a three-layer map to illustrate how the circulation of short-term funding, collateral, and assets may spread financial stability risks throughout the U.S. financial system. Potential vulnerabilities and contagion paths emerge as large banks, hedge funds, central clearinghouses, and other market participants become increasingly interconnected.

All flows of secured funding in the financial system are met by flows of collateral in the opposite direction. A network depicting secured funding flows thus implicitly reveals a network of collateral flows. Collateral can also be presented as its own network to show collateral arrangements with bilateral counterparties, triparty banks, and central counterparties; the purpose and incentives of collateral exchanges; and participants involved. We create a collateral map to show how this function of the financial system works, especially with secured funding and derivatives activity. This paper provides insights into the increased demand for collateral, the reduced capacity for banks to act as collateral intermediaries, and examples of risks and vulnerabilities in collateral flows.

During liquidity shocks such as occur when margin calls force the liquidation of leveraged positions, there is a widening disparity between the reaction speed of the liquidity demanders and the liquidity providers. Those who are forced to sell typically must take action within the span of a day, while those who are providing liquidity do not face similar urgency. Indeed, the flurry of activity and increased volatility of prices during the liquidity shocks might actually reduce the speed with which many liquidity providers come to the market. To analyze these dynamics, we build upon previous agent-based models of financial markets, and specifically the Preis et. al (Europhys Lett 75(3):510–516, 2006) model, to develop an order-book model with heterogeneity in trader decision cycles. The model demonstrates an adherence to important stylized facts such as a leptokurtic distribution of returns, decay of autocorrelations over moderate to long time lags, and clustering volatility. Consistent with empirical analysis of recent market events, we demonstrate the impact of heterogeneous decision cycles on market resilience and the stochastic properties of market prices.

Financial crises are often characterized by sharp reductions in liquidity followed by cascades of falling prices. Researchers are making progress in work to understand the levels of liquidity on a daily basis, but understanding the vulnerability of liquidity to market shocks remains a challenge. We develop an agent-based model with the objective of evaluating the market dynamics that lead the market supply of liquidity to recede during periods of crisis. The model uses a limit-order-book framework to examine the interaction of three types of traditional market agents: liquidity demanders, liquidity suppliers, and market makers. The paper highlights the implications of changes in market makers' ability to provide intermediation services and the heterogeneous decision cycles of liquidity demanders versus liquidity suppliers for crisis-induced illiquidity.

During liquidity shocks such as occur when margin calls force the liquidation of leveraged positions, there is a widening disparity between the reaction speed of the liquidity demanders and the liquidity providers. Those who are forced to sell typically must take action within the span of a day, while those who are providing liquidity do not face similar urgency. Indeed, the flurry of activity and increased volatility of prices during the liquidity shocks might actually reduce the speed with which many liquidity providers come to the market. To analyze these dynamics, we build upon previous agent-based models of financial markets to develop an order-book model with heterogeneity in trader decision cycles. The model demonstrates an adherence to important stylized facts such as a leptokurtic distribution of returns, decay of autocorrelations over moderate to long time lags, and clustering volatility. We show that the heterogeneity in decision cycles can increase the severity of market shocks, and even absent a shock can have notable effects on the stochastic properties of market prices.

Financial instability often results from positive feedback loops intrinsic to the operation of the financial system. The challenging task of identifying, modeling, and analyzing the causes and effects of such feedback loops requires a proper systems engineering perspective lacking in the remedies proposed in recent literature. We propose that signed directed graphs (SDG), a modeling methodology extensively used in process systems engineering, is a useful framework to address this challenge. The SDG framework is able to represent and reveal information missed by more traditional network models of financial system. This framework adds crucial information to a network model about the direction of influence and control between nodes, providing a tool for analyzing the potential hazards and instabilities in the system. This paper also discusses how the SDG framework can facilitate the automation of the identification and monitoring of potential vulnerabilities, illustrated with an example of a bank/dealer case study.

The dynamics of the financial system and the undercurrents of its vulnerabilities rest on the flow of funding. Analysts typically represent these dynamics as a network with banks and financial entities as the nodes and the funding links as the edges. This paper focuses instead on the funding operations within the nodes, in particular those within Bank/Dealers, adding a critical level of detail about potential funding risks. We present a funding map to illustrate the primary business activities and funding sources of a typical Bank/Dealer. We use that map to trace the paths of risk through four specific financial institutions during historical crises and to identify gaps in data needed for financial stability monitoring. We also introduce the concept of "funding durability," defined as the effective term of funding in the face of signaling and reputational considerations during periods of stress. Using these tools, the paper highlights the points of potential durability mismatch and resulting funding risks within the Bank/Dealer. It also provides insight into how funding weaknesses can pass from one institution to another and ultimately affect financial stability.

Stress testing, which has its roots in risk management, should be adapted to support financial stability monitoring and to incorporate the interconnections and dynamics of the financial system. Since the 2008 financial crisis, bank supervisors have honed their financial stability monitoring tools and significantly expanded the use of stress testing in the supervision of the largest financial institutions. This paper describes areas in which further research could contribute to the development of best practices in stress testing and how stress tests can be made more useful for macroprudential supervision. Both near-term and longer-term objectives are discussed.

Existing models of financial instability tend to be based on top-down, partial-equilibrium views of markets and their interactions; they are unable to incorporate the complexity of behavior among heterogeneous firms or the tendency for all types of firms to change their behavior during a crisis. This paper argues that agent-based models (ABMs)--which seek to explain how the behavior of individual firms or agents can affect outcomes in complex systems--can make an important contribution to our understanding of potential vulnerabilities and paths through which risks can propagate across the financial system.

“Insights” features the thoughts and views of the top authorities from academia and the profession. This section offers unique perspectives from the leading minds in investment management.

The Black Scholes formula has been widely used to price financial instruments. The derivation of this formula is based on the assumption of lognormally distributed returns which is often in poor agreement with actual data. An option pricing formula based on the generalized beta of the second kind (GB2) is presented. This formula includes the Black Scholes formula as a special case and accommodates a wide variety of nonlognormally distributed returns. The sensitivity of option values to departures from the skewness and kurtosis associated with the lognormal distribution is investigated.

This paper introduces a generalized distribution, called the GB2 distribution, for describing security returns. The distribution is extremely flexible, containing a large number of well-known distributions, such as the lognormal, log-t, and log-Cauchy distribu tions, as special or limiting cases and allowing large, even infinite, higher moments. This flexibility allows a direct representation of different degrees of fat tails in the distribution. The properties of the GB2 make it useful in empirical estimation of security returns and in facilitating the development of option-pricing models and other models that depend on the specification and mathematical manipulation of distributions. Copyright 1987 by the University of Chicago.