A Data Rich Money Market Model - Agent-based Modelling for Financial Stability

Paul Devine, Rahul Savani

2014

Abstract

Our position is that agent-based modelling is a potentially powerful complementary tool in the study of financial systems, especially where institutional behavioural factors and empirical data are incorporated. The work reported here concerns an agent-based model of a banking system focused on liquidity provision, principally the flows of cash between banks and other system actors. This model has been developed in conjunction with senior staff drawn from a major UK bank and consultancy and is highly data-rich in comparison with previous theoretical work in the field. Agents and relationships reflect practitioners’ views of the system and it incorporates institutional balance sheet representations, financial instruments together with real-world data collated from a range of sources. The bank agents in the model possess heterogeneous behaviours and data content drawn from real bank data. We report preliminary studies of the dynamical behaviour of this system in the context of the types of systemic shocks and perturbations observed in the real world. We review results which model the impact on a bank of a perceived lowering of its creditworthiness. These dynamics are not the result of endogenous assessments of the bank’s position, but the interplay of other banks’ and actor’s responses with its own behaviour.

References

  1. Acharya, V. V. and Merrouche, O. (2012). Precautionary hoarding of liquidity and interbank markets: Evidence from the subprime crisis. Review of Finance.
  2. Affinito, M. (2012). Do interbank customer relationships exist? and how did they function in the crisis? learning from italy. Journal of Banking & Finance, 36(12):3163 - 3184. Systemic risk, Basel III, global financial stability and regulation.
  3. Allen, F. and Gale, D. (2000). Financial contagion. The Journal of Political Economy, 108(1):1-33.
  4. Bagehot, W. (1873). Lombard Street: A Description of the Money Market. London:HS King, London.
  5. Biancotti, C., D'Aurizio, L., Ilardi, G., and Terna, P. (2009). Modelling an rtgs system with slapp. Technical report.
  6. BIS (2013). Basel iiii the liquidity coverage ratio and liquidity risk monitoring tools. Technical report, Bank of International Settlements.
  7. BIS (2014). Basel iii: The net stable funding ratio. Technical report, Bank of International Settlements.
  8. Burrows, O., Learmouth, D., McKeown, J., and Williams, R. (2012). Ramsi: a top-down stress-testing model developed at the banks of england's. Technical report, Bank of England.
  9. EC (2012). Non-bank financial institutions: Assessment of their impact on the stability of the financial system. Technical report, European Commission DirectorateGeneral for Economic and Financial Affairs.
  10. Economist (2013). Bear in the china shop. Economist, 408.
  11. Farmer, J. D. and Foley, D. (2009). The economy needs agent-based modelling. Nature, 460:685-686.
  12. Galbiati, M. and Soramki, K. (2011). An agent-based model of payment systems. Journal of Economic Dynamics and Control, 35(6):859-875.
  13. Gallegati, M., Giulioni, G., and Kichiji, N. (2003). Complex dynamics and financial fragility in an agentbased model. Advances in Complex Systems (ACS), 6(03):267-282.
  14. Geanakoplos, J., Axtell, R., Farmer, D., Howitt, P., Conlee, B., Goldstein, J., Hendrey, M., and Palmer, Nathan amd Yang, C.-Y. (2012). Getting at systemic risk via an agent-based model of the housing market. Technical report, Cowles Foundation.
  15. Giannone, D., Lanza, M., Pill, H., and Reichlin, L. (2012). The ecb and the interbank market. Technical report, European Central Bank.
  16. Honohan, P. and Klingebiel, D. (2003). The fiscal cost implications of an accommodating approach to banking crises. Journal of Banking & Finance, 27:1539-1560.
  17. Matsuoka, T. (2012). Imperfect interbank markets and the lender of last resort. Journal of Economic Dynamics and Control, 36(11):1673 - 1687.
  18. Railsback, S. F., Lytinen, S. L., and Jackson, S. K. (2006). Agent-based simulation platform: Review and development recommendations. In (Railsback et al., 2006), pages 609-623.
  19. Thornton, M. K. and Thornton, R. L. (1990). The financial crisis of a.d. 33: A keynsian depression? The Journal of Economic History, 50:655-662.
  20. van den End, J. W. and Tabbae, M. (2012). When liquidity risk becomes a systemic issue: Empirical evidence of bank behaviour. Journal of Financial Stability, 8(2):107 - 120.
Download


Paper Citation


in Harvard Style

Devine P. and Savani R. (2014). A Data Rich Money Market Model - Agent-based Modelling for Financial Stability . In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-038-3, pages 231-236. DOI: 10.5220/0005096602310236


in Bibtex Style

@conference{simultech14,
author={Paul Devine and Rahul Savani},
title={A Data Rich Money Market Model - Agent-based Modelling for Financial Stability},
booktitle={Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2014},
pages={231-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005096602310236},
isbn={978-989-758-038-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - A Data Rich Money Market Model - Agent-based Modelling for Financial Stability
SN - 978-989-758-038-3
AU - Devine P.
AU - Savani R.
PY - 2014
SP - 231
EP - 236
DO - 10.5220/0005096602310236