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Liquidity Saving in CHAPS: A Simulation Study

Liquidity Saving in CHAPS: A Simulation Study

Joanna McLafferty, Edward Denbee
Copyright: © 2013 |Pages: 23
ISBN13: 9781466620117|ISBN10: 1466620110|EISBN13: 9781466620124
DOI: 10.4018/978-1-4666-2011-7.ch007
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MLA

McLafferty, Joanna, and Edward Denbee. "Liquidity Saving in CHAPS: A Simulation Study." Simulation in Computational Finance and Economics: Tools and Emerging Applications, edited by Biliana Alexandrova-Kabadjova, et al., IGI Global, 2013, pp. 120-142. https://doi.org/10.4018/978-1-4666-2011-7.ch007

APA

McLafferty, J. & Denbee, E. (2013). Liquidity Saving in CHAPS: A Simulation Study. In B. Alexandrova-Kabadjova, S. Martinez-Jaramillo, A. Garcia-Almanza, & E. Tsang (Eds.), Simulation in Computational Finance and Economics: Tools and Emerging Applications (pp. 120-142). IGI Global. https://doi.org/10.4018/978-1-4666-2011-7.ch007

Chicago

McLafferty, Joanna, and Edward Denbee. "Liquidity Saving in CHAPS: A Simulation Study." In Simulation in Computational Finance and Economics: Tools and Emerging Applications, edited by Biliana Alexandrova-Kabadjova, et al., 120-142. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2011-7.ch007

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Abstract

This study uses a simulation methodology and real payment data to quantify the liquidity efficiency that could be obtained in CHAPS, the UK’s large-value payment system, by the implementation of a Liquidity Saving Mechanism (LSM). The payment data comes from payments submitted to CHAPS and, as such, reflects bank behaviour in a system without an LSM. The authors use survey data about urgent payments and bilateral limits to calibrate the simulations and make reasonable assumptions about how banks might behave in a system with an LSM. The simulations show that introducing an offsetting algorithm into the existing Real Time Gross Settlement (RTGS) set-up could result in significant liquidity savings. The authors find that liquidity savings could be of the order of 30% compared to a simple RTGS. The results suggest that the benefits, however, are unevenly distributed amongst the members: some benefit more than others. In line with other studies, there is a trade-off between liquidity savings and payment delay. Delays range from a couple of minutes to over two hours, depending upon the delay measure and the LSM set-up. Each bank will have to choose its optimal position on the savings-delay curve, depending upon the relative weight it gives to liquidity usage and payment timing. Furthermore, the authors simulate a range of different algorithms and find that liquidity savings are almost invariant across algorithms. This suggests that liquidity saving is driven by the structural imposition of two payment streams and the restriction of liquidity to non-urgent payments as opposed to the sophistication of an offsetting algorithm. The choice of algorithm does, however, have a major impact upon the size of delay that is introduced for a given liquidity saving. In practice, banks may choose to translate some of this reduction in delay into an increase in liquidity saving.

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