IDEAS home Printed from https://ideas.repec.org/p/boe/boeewp/0950.html
   My bibliography  Save this paper

A CBA of APC: analysing approaches to procyclicality reduction in CCP initial margin models

Author

Listed:
  • Murphy, David

    (London School of Economics and Political Science)

  • Vause, Nicholas

    (Bank of England)

Abstract

Following a period of relative calm, many derivative users received large margin calls as financial market volatility spiked amid the onset of the Covid‑19 global pandemic in March 2020. This reinvigorated the policy debate about dampening such ‘procyclicality’ of margin requirements. In this paper, we suggest how margin setters and policymakers might measure procyclicality and target particular levels of it. This procyclicality management involves recalibrating margin model parameters or applying anti-procyclicality (APC) tools. Different options reduce procyclicality by varying amounts, and do so at different costs, which we measure using the average additional margin required over the cycle. Thus, we perform a cost-benefit analysis (CBA) of the different options. We illustrate our approach using a popular type of margin model – filtered historical simulation value-at-risk – on simple portfolios, presenting the costs and benefits of varying a key model parameter and applying a number of different APC tools, including those in European legislation.

Suggested Citation

  • Murphy, David & Vause, Nicholas, 2021. "A CBA of APC: analysing approaches to procyclicality reduction in CCP initial margin models," Bank of England working papers 950, Bank of England.
    • Handle: RePEc:boe:boeewp:0950
    as

    Download full text from publisher

    File URL: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2021/a-cba-of-apc-analysing-approaches-to-procyclicality-reduction-in-ccp-initial-margin-models.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Bakoush, Mohamed & Gerding, Enrico H. & Wolfe, Simon, 2019. "Margin requirements and systemic liquidity risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 78-95.
    3. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    4. Gurrola-Perez, Pedro & Murphy, David, 2015. "Filtered historical simulation Value-at-Risk models and their competitors," Bank of England working papers 525, Bank of England.
    5. Murphy, David & Vasios, Michalis & Vause, Nick, 2014. "Financial Stability Paper No 29: An investigation into the procyclicality of risk-based initial margin models," Bank of England Financial Stability Papers 29, Bank of England.
    6. Paul Glasserman & Qi Wu, 2018. "Persistence and Procyclicality in Margin Requirements," Management Science, INFORMS, vol. 64(12), pages 5705-5724, December.
    7. Rama Cont & Eric Schaanning, 2017. "Fire sales, indirect contagion and systemic stress testing," Working Paper 2017/2, Norges Bank.
    8. Mateusz Buczyński & Marcin Chlebus, 2019. "Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states," Working Papers 2019-12, Faculty of Economic Sciences, University of Warsaw.
    9. Murphy, David & Vasios, Michalis & Vause, Nicholas, 2016. "A comparative analysis of tools to limit the procyclicality of initial margin requirements," Bank of England working papers 597, Bank of England.
    10. Eric A. Posner & E. Glen Weyl, 2014. "Benefit-Cost Paradigms in Financial Regulation," The Journal of Legal Studies, University of Chicago Press, vol. 43(S2), pages 1-34.
    11. Filippo Curti & Ibrahim Ergen & Minh Le & Marco Migueis & Rob T. Stewart, 2016. "Benchmarking Operational Risk Models," Finance and Economics Discussion Series 2016-070, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dávid Zoltán Szabó & Kata Váradi, 2022. "Margin requirements based on a stochastic correlation model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1797-1820, October.
    2. Sirio Aramonte & Andreas Schrimpf & Hyun Song Shin, 2023. "Non-bank financial intermediaries and financial stability," Chapters, in: Refet S. Gürkaynak & Jonathan H. Wright (ed.), Research Handbook of Financial Markets, chapter 7, pages 147-170, Edward Elgar Publishing.
    3. Grothe, Magdalena & Pancost, N. Aaron & Tompaidis, Stathis, 2023. "Collateral competition: Evidence from central counterparties," Journal of Financial Economics, Elsevier, vol. 149(3), pages 536-556.

    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. Dávid Zoltán Szabó & Kata Váradi, 2022. "Margin requirements based on a stochastic correlation model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1797-1820, October.
    2. Denisa Banulescu-Radu & Christophe Hurlin & Jérémy Leymarie & Olivier Scaillet, 2021. "Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures," Management Science, INFORMS, vol. 67(9), pages 5730-5754, September.
    3. Lannoo, Karel & Thomadakis, Apostolos, 2020. "Derivatives in Sustainable Finance," ECMI Papers 29791, Centre for European Policy Studies.
    4. 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.
    5. Li, Dan & Clements, Adam & Drovandi, Christopher, 2023. "A Bayesian approach for more reliable tail risk forecasts," Journal of Financial Stability, Elsevier, vol. 64(C).
    6. Farkas, Walter & Fringuellotti, Fulvia & Tunaru, Radu, 2020. "A cost-benefit analysis of capital requirements adjusted for model risk," Journal of Corporate Finance, Elsevier, vol. 65(C).
    7. d’Addona, Stefano & Khanom, Najrin, 2022. "Estimating tail-risk using semiparametric conditional variance with an application to meme stocks," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 241-260.
    8. Jin, YangKyu & Suh, Sangwon, 2024. "Procyclical variation margins in central clearing," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    9. Chen, Yan & Yu, Wenqiang, 2020. "Setting the margins of Hang Seng Index Futures on different positions using an APARCH-GPD Model based on extreme value theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 544(C).
    10. Ning Zhang & Yujing Gong & Xiaohan Xue, 2023. "Less disagreement, better forecasts: Adjusted risk measures in the energy futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1332-1372, October.
    11. Berlinger, Edina & Dömötör, Barbara & Illés, Ferenc, 2019. "Anti-cyclical versus risk-sensitive margin strategies in central clearing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 117-131.
    12. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2022. "Estimating risks of option books using neural-SDE market models," Papers 2202.07148, arXiv.org.
    13. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    14. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    15. Rosa Ferrentino & Luca Vota, 2022. "A Mathematical Model for the Pricing of Derivative Financial Products: the Role of the Banking Supervision and of the Model Risk," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 11(1), pages 1-2.
    16. Fernández-Aguado, Pilar Gómez & Martínez, Eduardo Trigo & Ruíz, Rafael Moreno & Ureña, Antonio Partal, 2022. "Evaluation of European Deposit Insurance Scheme funding based on risk analysis," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 234-247.
    17. Václav Brož & Lukáš Pfeifer, 2021. "Are risk weights of banks in the Czech Republic procyclical? Evidence from wavelet analysis," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(1), pages 113-139.
    18. Tasneem Bani-Mustafa & Nicola Pedroni & Enrico Zio & Dominique Vasseur & Francois Beaudouin, 2020. "A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models," Journal of Risk and Reliability, , vol. 234(6), pages 748-763, December.
    19. Wu, Qi & Yan, Xing, 2019. "Capturing deep tail risk via sequential learning of quantile dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    20. Pfeifer, Lukáš & Hodula, Martin, 2021. "A profit-to-provisioning approach to setting the countercyclical capital buffer," Economic Systems, Elsevier, vol. 45(1).

    More about this item

    Keywords

    Central counterparty; cost-benefit analysis; derivatives clearing; initial margin models; mandatory clearing; procyclicality;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:boe:boeewp:0950. 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: Digital Media Team (email available below). General contact details of provider: https://edirc.repec.org/data/boegvuk.html .

    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.