IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03573058.html
   My bibliography  Save this paper

Tail Risk in a Retail Payments System

Author

Listed:
  • Leonard Sabetti
  • David Jacho-Chávez
  • Robert Petrunia
  • Marcel Voia

    (Carleton University)

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

  • Leonard Sabetti & David Jacho-Chávez & Robert Petrunia & Marcel Voia, 2018. "Tail Risk in a Retail Payments System," Post-Print hal-03573058, HAL.
    • Handle: RePEc:hal:journl:hal-03573058
    DOI: 10.1515/jbnst-2018-0024
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rostagno, Luciano Martin, 2005. "Empirical tests of parametric and non-parametric Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) measures for the Brazilian stock market index," ISU General Staff Papers 2005010108000021878, Iowa State University, Department of Economics.
    2. Carol Alexander & José María Sarabia, 2012. "Quantile Uncertainty and Value‐at‐Risk Model Risk," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1293-1308, August.
    3. Dixon Jay & Petrunia Robert & Rollin Anne-Marie, 2018. "Studying Firm Growth Distributions with a Large Administrative Employment Database," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 238(3-4), pages 189-221, July.
    4. Azizpour, S & Giesecke, K. & Schwenkler, G., 2018. "Exploring the sources of default clustering," Journal of Financial Economics, Elsevier, vol. 129(1), pages 154-183.
    5. Gustavo Canavire-Bacarreza & Luis C. Carvajal-Osorio, 2020. "Two Stories of Wage Dynamics in Latin America: Different Policies, Different Outcomes," Journal of Labor Research, Springer, vol. 41(1), pages 128-168, June.
    6. Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2004. "The Use of GARCH Models in VaR Estimation," MPRA Paper 96332, University Library of Munich, Germany.
    7. Carol Alexander & Jose Maria Sarabia, 2010. "Endogenizing Model Risk to Quantile Estimates," ICMA Centre Discussion Papers in Finance icma-dp2010-07, Henley Business School, University of Reading.
    8. Danielsson, Jon & James, Kevin R. & Valenzuela, Marcela & Zer, Ilknur, 2016. "Model risk of risk models," Journal of Financial Stability, Elsevier, vol. 23(C), pages 79-91.
    9. Ante Babić & Ante Žigman, 2001. "Currency Crises: Theoretical and Empirical Overview of the 1990s," Surveys 5, The Croatian National Bank, Croatia.
    10. Xiongwei Ju & Neil D. Pearson, 1998. "Using Value-at-Risk to Control Risk Taking: How Wrong Can you Be?," Finance 9810002, University Library of Munich, Germany.
    11. Mei-Ling Tang & Trung K. Do, 2019. "In search of robust methods for multi-currency portfolio construction by value at risk," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(1), pages 107-126, March.
    12. de Araújo, André da Silva & Garcia, Maria Teresa Medeiros, 2013. "Risk contagion in the north-western and southern European stock markets," Journal of Economics and Business, Elsevier, vol. 69(C), pages 1-34.
    13. Ryohei Kawata & Masaaki Kijima, 2007. "Value-at-risk in a market subject to regime switching," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 609-619.
    14. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    15. Giamouzi, Maria & Nomikos, Nikos K, 2021. "Identifying shipowners’ risk attitudes over gains and losses: Evidence from the dry bulk freight market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    16. Huynh, Kim P. & Jacho-Chávez, David T. & Kryvtsov, Oleksiy & Shepotylo, Oleksandr & Vakhitov, Volodymyr, 2016. "The evolution of firm-level distributions for Ukrainian manufacturing firms," Journal of Comparative Economics, Elsevier, vol. 44(1), pages 148-162.
    17. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    18. Huang, Alex YiHou, 2010. "An optimization process in Value-at-Risk estimation," Review of Financial Economics, Elsevier, vol. 19(3), pages 109-116, August.
    19. Marios Nerouppos & David Saunders & Costas Xiouros & Stavros A. Zenios, 2006. "Risk Management in Emerging Markets: Practical Methodologies and Empirical Tests," Multinational Finance Journal, Multinational Finance Journal, vol. 10(3-4), pages 179-221, September.
    20. Hongtao Guo & Miranda S. Lam & Guojun Wu & Zhijie Xiao, 2013. "Risk Analysis Using Regression Quantiles: Evidence from International Equity Markets," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 7(2), pages 1-15.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-03573058. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.