An agent-based model for financial vulnerability

Abstract

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.

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Fig. 1

Source: Office of financial research annual report (2012), pp. 56–57

Fig. 2

Source: Authors’ analysis

Fig. 3

Source: Bigbee et al. (2015)

Fig. 4

Source: Authors’ analysis, Bigbee et al. (2015)

Fig. 5

Source: Authors’ model, Bigbee et al. (2015)

Fig. 6

Source: Authors’ model, Bigbee et al. (2015)

Fig. 7

Source: Authors’ analysis, Bigbee et al. (2015)

Fig. 8

Source: Authors’ analysis, Bigbee et al. (2015)

Fig. 9
Fig. 10

Source: Authors’ analysis

Fig. 11
Fig. 12
Fig. 13

Notes

  1. 1.

    Though we term this agent as a hedge fund, it can more broadly represent other institutional financial firm classes, which have varying degrees of leverage in their portfolio.

  2. 2.

    Bigbee et al. (2015).

  3. 3.

    See the Supplementary Material for additional results from our robustness testing.

  4. 4.

    See Summers et al. (1999) and Bookstaber (2007) for a first-hand account of one path of the contagion over the course of the Russian default to the failure of Long-Term Capital Management.

  5. 5.

    The parameter values used in this section are the same as used in Sect. 4.

  6. 6.

    Each edge in the network denotes the relational impact of one node, i, on another, j, based on the relationship that exists in the agent-based model, normalized by running the simulation a number of times with variations on each variable. The width of the edge shows the cumulative effect of the transmission with respect to t periods and the n runs of the simulation, and the color of the edge in the figure shows the intensity of the interaction in the current period; a darker color means greater intensity or change in the system relative to other runs and periods observed.

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Authors

Corresponding author

Correspondence to Mark Paddrik.

Additional information

We wish to thank additional members of the MITRE team: Zoe Henscheid, David Slater, Matthew Koehler, Tony Bigbee, Matt McMahon, and Christine Harvey. We also would like to thank Nathan Palmer for his work on the conceptual model. Finally, we would like to thank Charlie Brummitt, Paul Glasserman, Benjamin Kay, Blake LeBaron, Eric Schaanning, Larry Wall, and participants of MIT’s Consortium for Systemic Risk Analysis 2013, the INET Conference Toronto 2014, and Atlanta Federal Reserve’s Conference on Nonbank Financial Firms and Financial Stability for their valuable comments.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (docx 6889 KB)

Appendix

Appendix

See Tables 6 and 7.

Table 6 Model variable glossary
Table 7 Model parameters

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Bookstaber, R., Paddrik, M. & Tivnan, B. An agent-based model for financial vulnerability. J Econ Interact Coord 13, 433–466 (2018). https://doi.org/10.1007/s11403-017-0188-1

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Keywords

  • Agent-based models
  • Financial intermediation
  • Financial networks
  • Contagion
  • Macroprudential
  • Stress testing

JEL Classification

  • G01
  • G14