CONCLUSION

Which is the optimal solution in the trade-off between realism and complexity in an Agent Based Model? Answering this question seems to become more and more important since Agent Based Models may become a standard tool for Central Banks to understand complex interac­tions of market participants and of several events from perspectives other than those offered by the mainstream tools.

Under these conditions, new application fields could be imagined for Agent Based Modeling, which could be addressed in the future to Central Banking issues. ABM could, for instance, be used to assess the effectiveness and the risks of the unconventional measures introduced from central banks as a response to the crisis (e.g. BoE’s and FED’s quantitative easing, ECB’s Securities Mar­ket Program, and so on). It could be also applied to evaluate the impacts and the second round effects of new regulations, which are currently discussed on both sides of the Atlantic (e.g. the new EU regulation, the Dodd-Frank Act) and fostered by all international fora (FSRB, G20, and so one). Among these cases, ABM seems to be potentially fruitful in assessing the possible impact of banks delaying payments in response to the introduction of regulatory requirements, as e.g.

REFERENCES

Angelini, P. (1998). An analysis of competitive externalities in gross settlement systems. Journal of Banking & Finance, 22(1), 1-18. doi:10.1016/ S0378-4266(97)00043-5

Angelini, P. (2002). Are banks risk-averse? Timing of the operations in the interbank market. Journal of Money, Credit and Banking, 32(3), 54-73.

Angelini, P., Maresca, G., & Russo, D. (1996). Sys­temic risk in the netting system. Journal of Banking & Finance, 20(11), 853-868. doi:10.1016/0378- 4266(95)00029-1

Arciero, L., Biancotti, C., DAurizio, L., & Im- penna, C. (2008). Exploring agent-based methods for the analysis of payment systems: A crisis model for StarLogo TNG. Journal of Artificial Societies and Social Simulation. 12(1), 2. Re­trieved May 12, 2011, from http://jasss.soc.surrey. ac.uk∕12∕1∕2.html

Arciero, L., DAurizio, L., Ilardi, G., Picillo, C., & Terna, P. (2010). Modelling banks’ treasurers behaviours in an RTGS system. Paper presented at 8th Bank of Finland Simulator Seminar and Workshop. Helsinki, Finland.

Armantier, O., McAndrews, J., & Arnold, J. (2008). Changes in the timing distribution of fedwire funds transfers. Economic Policy Review, 14(2), 83-112.

Ashcraft, A. B., & Darrell, D.

Ball, A., Denbee, E., Manning, M. J., & Wetherilt, A. (2011). Intraday liquidity: Risk and regulation. Bank of England Financial Stability Paper No. 11. London, UK: Bank of England. Retrieved Novem­ber 15, 2011, from http://www.bankofengland. co.uk/publications/fsr/fs_paper11.pdf

Bech, M., & Garrat, R. (2003). The intraday li­quidity management game. Journal of Economic Theory, 109(2), 198-219. doi:10.1016/S0022- 0531(03)00016-4

Bech, M., Madsen, B., & Natorp, L. (2002). Systemic risk in the Danish interbank netting system. Danmarks Nationalbank Working Papers 2002. Copenhagen, Denmark: Danmarks Na­tionalbank. Retrieved November 15, 2011, from http://royalmint.dk/C1256BE9004F6416/side/ WP_82002_Systemic_Risk_in_the_Danish_In- terbank_Netting_System/$file/wp_8_2002.pdf

Bedford, P., Millard, S., & Yang, J. (2004). As­sessing operational risk in CHAPS sterling: A simulation approach. Bank of England Financial Stability Report, 16, 135-143.

Beyeler, W. E., Glass, R. J., Bech, M. L., & Soramaki, K. (2006). Congestion and cascades in payment systems. StaffReports 259. New York, NY: Federal Reserve Bank of New York.

BIS. (1997). Real-time gross settlement systems. Basel, Switzerland: Bank for International Settle­ments.

BIS. (2001). Core principles for systemically important payment systems. Basel, Switzerland: Bank for International Settlements.

Cassola, N. (2008). The reserve fulfilment path of Euro area commercial banks: Empirical testing using panel data. ECB Working Paper No. 869. Frankfurt, Germany: European Central Bank. Retrieved November 15, 2011, from http://www. ecb.europa.eu/pub/pdf/scpwps/ecbwp869.pdf

Galbiati, M., & Soramaki, K. (2010). Liquidity­saving mechanisms and bank behavior. Bank of England Working Papers 400. London, UK: Bank of England. Retrieved November 15, 2011, from http://www.bankofengland.co.uk/publications/ workingpapers/wp400.pdf

Galbiati, M., & Soramaki, K.

Glaser, M., & Haene, P. (2009). Liquidity effects of a participant-level operational disruption in the Swiss interbank clearing system. In H. Leinonen (Ed.), Simulation Analyses and Stress Testing of Payment Network (pp. 297-320). Helsinki, Fin­land: Bank of Finland.

Haldane, A. G. (2009). Small lessons from a big crisis. Paper presented at the Federal Reserve Bank of Chicago 45th Annual Conference “Re­forming Financial Regulation.” Retrieved August, 29, 2011, from http://www.bankofengland.co.uk/ publications∕speeches∕2009∕speech397.pdf

Hejimans, R. (2009). Simulations in the Dutch interbank payment system: A sensitivity analysis. De Nederlandsche Bank Working Papers 19.9. Amsterdam, The Netherlands: De Nederlandsche Bank. Retrieved November 15, 2011, from http:// www.dnb.nl/binaries/Working%20paper%20 199_tcm46-211050.pdf

Hellqvist, M. (2010). BoF-PSS2 simulator, cur­rent situation and recent developments. Paper presented at 8th Bank of Finland Simulator Seminar and Workshop. Helsinki, Finland. Retrieved May 12, 2011 from http://pss.bof.fi/ Documents/01_Hellqvist_Current_situation.pdf

Humphrey, D. B. (1986). Payments finality and risk of settlement failure. In A. Saunders, & L.

J. White (Eds.), Technology and the Regulation of Financial Markets: Securities, Futures, and Banking (pp. 97-120). Lexington, MA: Heath.

Klee, E. (2010). Operational outages and aggregate uncertainty in the federal funds market. Journal of Banking & Finance, 34(10), 2386-2402. doi:10.1016/j.jbankfin.2010.03.002

Kuussaari, H. (1996). Systemic risk in the Finnish payment system: An empirical investigation. Bank of Finland Discussion Paper 3/1996. Helsinki, Finland: Bank of Finland.

Ledrut, E. (2007). Simulating retaliation in payment systems: Can banks control their expo­sure to a failing participant.

Leinonen, H., & Soramaki, K. (1999). Optimizing liquidity usage and settlement speed in payment systems. Bank of Finland Discussion Paper 16/99. Helsinki, Finland: Bank of Finland.

Markose, S., Alentorn, A., Millard, S., & Yang, J. (2011). Designing large value payment systems: An agent-based approach. University of Essex Economics Department Discussion Paper No. 700. Colchester, UK: University of Essex.

McAndrews, J., & Potter, S. M. (2002). Liquid­ity effects of the events of September 11, 2001. FRBNYEconomic Policy Review, 11, 59-79.

McAndrews, J., & Rajan, S. (1999). The timing and sources of funding of fedwire funds transfers. Federal Reserve Bank of New York Working Paper. New York, NY: Federal Reserve Bank of New York.

McAndrews, J., & Trundle, J. M. (2001). New pay­ment system designs: Causes and consequences. Bank of England Financial Stability Review, 11, 127-136.

McAndrews, J., & Wasilyew, G. (1995). Simula­tions of failure in a payment system. Economic Research Division Working Paper 95-19. Philadel­phia, PA: Federal Reserve Bank of Philadelphia.

ENDNOTES

¹ The opinions expressed herein are those of authors and do not commit the Banca d’Italia.

Some RTGS systems provide participants with queue management facilities which resubmit automatically payment without coverage every time an increase in the debited settlement account occurs (see BIS, 1997). These kinds of simulation exercises have been carried out with reference to DNS system operating in several countries: see e.g. Humphrey (1986), Angelini, Maresca, and Russo (1995), Kuussaari (1996), and on the Finnish interbank payment system, McAndrews and Wasilew (1995), Bech, et al. (2002).

To come back to the Angelini model: 1) the restriction only two banks and two periods (morning and afternoon); 2) the absence of reserve requirements and transaction costs related to borrowing or lending; 3) the risk of neutrality of agents.

The Financial Network Analyzer (FNA) de­veloped by K. Soramaki is another example of payment system simulator.

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It should be noted that, in principle, the BoF-PSS2 can be enriched with behavioral functions, but this requires developing ad­ditional routines to be performed separately. An elegant but not definitive attempt to introduce behavioral reactions in a model simulating outages at participant level is carried out by Ledrut (2007), who assumes that banks stop sending payment to the hit participant when their exposure breach a pre-determined threshold.

Besides the cases discussed in this chapter other contributions are provided by Gal- biati and Soramaki (2010) and Beyeler et al. (2007).

The authors assume that outgoing payments stem from liabilities, which are negotiated in other departments in the bank and afterwards sent to the treasury for immediate settlement. Two settlement methods for the queued payments are tested both in the “opening liquidity” as well as in the “just-in-time” set up: first in first out criteria and a priority based on the size of the queued outflows. The first one appears to be the most efficient.

The discussed model extension is forthcom­ing by Arciero, D 'Aurizio, Ilardi, Picillo, and Terna.

A possible reference for an electronic platform for money market exchanges is the e-MID market: managed by an Italian company, e-MID SIM; this platform allows European banks to exchange deposits in euro from overnight to twelve months, by posting transparently on the screen their intention to buy or sell liquidity.

Without modeling the Central Bank and its monetary policy decisions, the system that can be simulated is similar to a framework in which the Central Bank has committed to keep rates unchanged so that the banks do not have interest rate expectations. The interest rate will, therefore, be expected to follow a mean-reverting process centred in the policy rate.

This work was previously published in Simulation in Computational Finance and Economics, edited by Biliana Alexandrova- Kabadjova, Serafin Martinez-Jaramillo, Alma Lilia Garcia-Almanza and Edward Tsang, pages 84-102, copyright 2013 by Business Science Reference (an imprint of IGI Global).

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