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Tail Risk in a Retail Payments System

Author

Listed:
  • Sabetti Leonard

    (Payments Canada, 350 Albert St #800, Ottawa, ON K1R 1A4)

  • Jacho-Chávez David T.

    (Department of Economics, Emory University, 1602 Fishburne Dr, Rich Bldg., 3rd Floor, Atlanta, GA 30322, USA)

  • Petrunia Robert

    (Department of Economics, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada)

  • Voia Marcel C.

    (Department of Economics, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada, K1S 5B6)

Abstract

In this paper, we study a credit risk (collateral) management scheme for the Canadian retail payment system designed to cover the exposure of a defaulting member. We estimate ex ante the size of a collateral pool large enough to cover exposure for a historical worst-case default scenario. The parameters of the distribution of the maxima are estimated using two main statistical approaches based on extreme value models: Block-Maxima for different window lengths (daily, weekly and monthly) and Peak-over-Threshold. Our statistical model implies that the largest daily net debit position across participants exceeds roughly $1.5 billion once a year. Despite relying on extreme-value theory, the out of sample forecasts may still underestimate an actual exposure given the absence of observed data on defaults and financial stress in Canada. Our results are informative for optimal collateral management and system design of pre-funded retail-payment schemes.

Suggested Citation

  • Sabetti Leonard & Jacho-Chávez David T. & Petrunia Robert & Voia Marcel C., 2018. "Tail Risk in a Retail Payments System," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 238(3-4), pages 353-369, July.
    • Handle: RePEc:jns:jbstat:v:238:y:2018:i:3-4:p:353-369:n:5
    DOI: 10.1515/jbnst-2018-0024
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    References listed on IDEAS

    as
    1. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Proceedings 512, Federal Reserve Bank of Chicago.
    2. Héctor Pérez Saiz & Gabriel Xerri, 2016. "Credit Risk and Collateral Demand in a Retail Payment System," Discussion Papers 16-16, Bank of Canada.
    3. Danielsson, Jon & Zhou, Chen, 2015. "Why risk is so hard to measure," LSE Research Online Documents on Economics 62002, London School of Economics and Political Science, LSE Library.
    4. Younes Bensalah, 2000. "Steps in Applying Extreme Value Theory to Finance: A Review," Staff Working Papers 00-20, Bank of Canada.
    5. Huynh, Kim P. & Jacho-Chávez, David T. & Petrunia, Robert J. & Voia, Marcel, 2011. "Functional Principal Component Analysis of Density Families With Categorical and Continuous Data on Canadian Entrant Manufacturing Firms," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 858-878.
    6. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, vol. 2(Apr), pages 39-69.
    7. Michael Tompkins & Ariel Olivares, 2016. "Clearing and Settlement Systems from Around the World: A Qualitative Analysis," Discussion Papers 16-14, Bank of Canada.
    8. John Galbraith & Serguei Zernov, 2009. "Extreme dependence in the NASDAQ and S&P 500 composite indexes," Applied Financial Economics, Taylor & Francis Journals, vol. 19(13), pages 1019-1028.
    9. Kim Huynh & David Jacho-Chávez & Robert Petrunia & Marcel Voia, 2015. "A nonparametric analysis of firm size, leverage and labour productivity distribution dynamics," Empirical Economics, Springer, vol. 48(1), pages 337-360, February.
    10. John W. Galbraith & Greg Tkacz, 2013. "Analyzing Economic Effects of September 11 and Other Extreme Events Using Debit and Payments System Data," Canadian Public Policy, University of Toronto Press, vol. 39(1), pages 119-134, March.
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