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Volume 6 Number 4 June 2018

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Financial Market Infrastructures

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■ A CCP is a CCP is a CCP Robert T. Cox and Robert S. Steigerwald

■ Freeriding on liquidity in the Colombian large-value payment system Constanza Martínez and Freddy Cepeda

■ Measuring system-wide resilience of central counterparties Stathis Tompaidis

FORUM ■ One for my baby (and one more for the

road): incentives, default waterfalls and central counterparty skin-in-the-game Rebecca Lewis and John McPartland

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The Journal of Financial Market InfrastructuresEDITORIAL BOARD

Editor-in-ChiefRon Berndsen LCH and Tilburg University

Associate Editors

Sujit Chakravorti The Clearing HouseMassimo Cirasino World BankRobert Cox Federal Reserve Bank of ChicagoAnneke Kosse De Nederlandsche BankRodney Garratt University of CaliforniaTerry Goh Monetary Authority of SingaporeGerard Hartsink Global Legal Entity

Identifier FoundationRichard Heckinger Federal Reserve Bank

of Chicago (former)Ronald Heijmans DNBLex Hoogduin University of AmsterdamCharles Kahn University of IllinoisThorsten Koeppl Queen’s UniversityEsmond Lee Hong Kong Monetary AuthorityGottfried Leibbrandt SWIFTCarlos León Central Bank of Colombia

Klaus Löber European Central BankMark Manning Euroclear SA/NVAlistair Milne Loughborough

UniversityMasayuki Mizuno Bank of JapanDaniela Russo European Central BankEdwin Schooling Latter Financial

Conduct Authority (UK)Manmohan SinghJeff Stehm Promontory Financial GroupLawrence Sweet Federal Reserve Bank

of New YorkJohn Trundle Euroclear UK & IrelandLeo Van Hove Free University of

BrusselsWolf Wagner Erasmus UniversityFroukelien Wendt

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The Journal of

Financial MarketInfrastructures

The journalToday, in the light of the financial crisis, it has become part of the political agendato strengthen payment, clearing and settlement systems, as well as repositories fordata on the trades they process. In 2012 a new set of internationally agreed CPSS-IOSCO Principles carved out financial market infrastructures (FMIs) as a distinctarea in financial policy. The Journal of Financial Market Infrastructures is the firstjournal to focus on this exciting and dynamic sector, and aims to bring together acommunity of contributors from the constituent sectors to analyze FMIs to further thedevelopment of this emerging field. The journal provides a balanced representation ofacademic and practitioner-focused papers that are dedicated to analyzing operationaland regulatory effectiveness and efficiency of payment, clearing, settlement and traderepository systems; and the risks they manage, transmit and create.

The Journal of Financial Market Infrastructures considers submissions in theform of technical papers and policy-oriented papers (forum discussions) on topicsincluding, but not limited to, the following:

� systemically important payment systems,� securities settlement systems,� central counterparties,� central securities depositories,� trade repositories,� settlement risk and other FMI-related risks including interdependencies,� infrastructure-related systemic risk,� network analysis of an FMI,� critical service providers and non-bank payment service providers,� correspondent banking,� FMI liquidity and collateral management,� exchanges and multilateral trading platforms,� oversight and supervision of FMIs, and� FMI-related standardization and legislation.




The Journal of Financial Market Infrastructures Volume 6/Number 4

CONTENTS

Letter from the Editor-in-Chief vii

RESEARCH PAPERSA CCP is a CCP is a CCP 1Robert T. Cox and Robert S. Steigerwald

Freeriding on liquidity in the Colombian large-value payment system 19Constanza Martínez and Freddy Cepeda

Measuring system-wide resilience of central counterparties 41Stathis Tompaidis

FORUM PAPEROne for my baby (and one more for the road): incentives, defaultwaterfalls and central counterparty skin-in-the-game 55Rebecca Lewis and John McPartland

Index of papers in the current volume 71

Editor-in-Chief: Ron Berndsen Subscription Sales Manager: Aaraa JavedPublisher: Nick Carver Global Key Account Sales Director: Michelle GodwinJournals Manager: Sarah Campbell Composition and copyediting: T&T Productions LtdEditorial Assistant: Ciara Smith Printed in UK by Printondemand-Worldwide

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LETTER FROM THE EDITOR-IN-CHIEF

Ron BerndsenLCH and Tilburg University

Welcome to the summer 2018 edition of The Journal of Financial Market Infras-tructures. Our final issue of Volume 6 contains three papers on central counterparties(CCPs) and one paper on large-value payment systems (LVPSs).

The first paper in this issue, “A CCP is a CCP is a CCP” by Robert T. Cox andRobert S. Steigerwald, provides the reader with an in-depth analysis of the differencesbetween a CCP and a bank.1 The authors analyze the different roles that capital, col-lateral and risk management play in both types of financial institution. As they state,banks are risk takers, at the very least because they undertake maturity transforma-tion, while CCPs are risk managers. Understanding these differences is essential forpolicy-making and international standard setting, not only for CCPs in the normalresilience mode but also for CCPs undergoing the extreme case of resolution. Coxand Steigerwald draw several important policy conclusions, one of which refers tothe significance of own capital: a commodity that is quantitatively crucial for banksbut plays a qualitative role for CCPs.

“Freeriding on liquidity in the Colombian large-value payment system” by Con-stanza Martínez and Freddy Cepeda, this issue’s second paper, takes us outside centralclearing and into the world of real-time gross settlement systems. Because of the rel-atively high liquidity need in those systems, banks rely heavily on incoming funds.Therefore, a free rider problem may exist if some of the participants consciouslydecide to delay their outgoing payments.2 The authors investigate the extent to whichthis problem occurs in the Colombian case. For the Colombian LVPS (the CUD sys-tem), they find some evidence of the free rider problem but conclude that the negativeeffect on liquidity provision in the payment system is small.

In “Measuring system-wide resilience of central counterparties”, our third paper,Stathis Tompaidis’s research transcends the level of individual financial market infra-structure to focus on the financial system level: the entire network of multiple CCPsand their participants. Stress testing is a daily task for a CCP, but it is still early daysfor stress testing at the system level. The author proposes a framework for extendingexisting stress test results obtained at individual CCPs to create a system-wide stress

1 A related contribution can be found in Manning, M. J., and Hughes, D. (2016). Central coun-terparties and banks: vive la difference. The Journal of Financial Market Infrastructures 4(3),1–24.2 A related contribution can be found in Diehl, M. (2013). Measuring freeriding in large-valuepayments systems: the case of TARGET2. The Journal of Financial Market Infrastructures 1(3),31–53.


vii

Journal of Financial Market Infrastructures 6(4)




test. That framework is compared with the two existing system-wide stress tests orga-nized by the US Commodity Futures Trading Commission (the US perspective) andthe European Securities Market Authority (the EU perspective). The paper concludeswith suggestions for improvements, such as substantially increasing the number ofstress scenarios.3

The issue’s fourth paper is a forum paper: “One for my baby (and one more forthe road): incentives, default waterfalls and central counterparty skin-in-the-game” byRebecca Lewis and John McPartland. In the case of for-profit CCPs, the authors argue,two tranches of their capital should be placed in the prefunded part of the waterfall,directly after the defaulters pay part (the junior tranche, or skin-in-the-game) anddirectly after the survivors pay part (the senior tranche, which would be at the veryend of the prefunded waterfall). Almost all CCPs considered in the paper presentlyhave the junior tranche but not the senior tranche. Lewis and McPartland also discussthe impact of changing the amount of skin-in-the-game. The arguments in favor oftwo tranches come from the balanced incentive structure for the CCP, its participantsand shareholders.4

3 Recently, CPMI–IOSCO published a report on this topic: “Framework for supervisory stress testingof central counterparties” (April 2018), see www.bis.org/cpmi/publ/d176.htm.4 Related contributions on skin-in-the-game are: Cox, R. T. (2015). Cave quid optes: waterfalls andcentral counterparty capital. The Journal of Financial Market Infrastructures 3(4), 63–71; Carter,L., and Garner, M. (2016). Skin in the game: central counterparty risk controls and incentives. TheJournal of Financial Market Infrastructures 4(3), 39–54; Murphy, D. (2017). I’ve got you undermy skin: large central counterparty financial resources and the incentives they create. The Journalof Financial Market Infrastructures 5(3), 1–18.

Journal of Financial Market Infrastructures 6(4)





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Journal of Financial Market Infrastructures 6(4), 1–18DOI: 10.21314/JFMI.2018.085

Research Paper

A CCP is a CCP is a CCP

Robert T. Cox and Robert S. Steigerwald

Financial Markets Group, Federal Reserve Bank of Chicago, 230 S. LaSalle, Chicago, IL 60604,USA; emails: [email protected], [email protected]

(Received June 16, 2017; revised October 10, 2017; accepted October 23, 2017)

ABSTRACT

Central counterparties (CCPs) are important financial market infrastructures. Theorderly risk management operations and financial resilience of CCPs and other mar-ket infrastructures are essential for financial stability. Regulators and other policymakers face a major challenge in constructing appropriate regulatory frameworksfor central clearing. The challenge of establishing standards for CCP risk manage-ment and resilience is made even more difficult by policy makers’ tendency to viewCCPs through the lens of bank regulation. This paper discusses the many differencesbetween CCPs and banks as well as the significance of these differences. In particular,we focus on differences in the roles that capital and collateral play in connection withCCP and bank risk management. From this discussion, we draw the following pol-icy conclusions. First, a CCP’s capital cannot be the primary (or even a significant)resource for loss absorption without fundamentally altering the incentive structureembedded in the default waterfall, if not the business model of the CCP itself. Sec-ond, capital analysis alone tells us little or nothing about the resilience of a givenCCP or its ability to recover from threats to its viability. Third, resolution planningfor CCPs must focus on features that are unique to central clearing.

Keywords: central counterparties (CCPs); financial markets regulation; banking regulation; CCPrecovery; CCP resolution.

Corresponding author: R. T. Cox Print ISSN 2049-5404 j Online ISSN 2049-5412© 2018 Infopro Digital Risk (IP) Limited

1 Journal of Financial Market Infrastructures






2 R. T. Cox and R. S. Steigerwald

1 INTRODUCTION

Banks, it is often said, are “special” (Corrigan 1983). Central counterparty (CCP)clearing houses are also special in that they can be systemically important (Bernanke1990, p. 1). However, banks and CCPs are special in very different ways.

CCPs are not banks (Cœuré 2015). They do not perform the central risk-taking func-tion that distinguishes banking: asset/liability (or maturity) transformation throughdeposit taking and lending, and related activities.1 The balance sheet of a CCP is“quite different from those of other major types of systemically important financialinstitutions such as banks, broker dealers, and insurance companies” (Duffie 2015; seealso Hughes and Manning 2015, 2016). Moreover, CCPs are not exchanges, deposi-tories or payment systems, although they have features that resemble insurance andmay have connections with other market infrastructures.

What are CCPs? In this paper, we claim that a CCP, viewed from an economicperspective, is a “commitment mechanism”. The ultimate function of a CCP is toassure the performance of contract obligations. It does so by becoming a substitutecounterparty to all trades submitted for clearing; becoming, in effect, “the buyer toevery seller and the seller to every buyer…thereby ensuring the performance of opencontracts” (CPSS–IOSCO 2012, p. 9). We argue that, given their unique role in afinancial market, CCPs require regulation tailored to their function; principles frombanking regulation are not necessarily appropriate for the regulation of CCPs.

2 DISCUSSION

CCPs have taken on an enhanced role in the global financial system since the globalfinancial crisis of 2008–9 and the Group of Twenty (G20) clearing mandate followingthe Pittsburgh Summit of 2009. In particular, the orderly operation and financial

1 We recognize that some CCPs are licensed as “credit institutions” (a legal requirement in someEuropean countries), and that two US CCPs were chartered as limited purpose trust companies.See, for example, “Multinet international bank order” (Board of Governors of the Federal ReserveSystem 1996) and “ICE US Trust LLC order” (Board of Governors of the Federal Reserve System2009). Therefore, CCPs may technically qualify as “banks” of one sort or another. That, however,is of no consequence for the purposes of the discussion in this paper. As explained above, CCPs –regardless of their form of charter or license – do not perform banking functions. Accordingly, asCœuré (2015) explains:

We should not try to apply blindly to CCPs the macroprudential tools we have devel-oped for the banking sector. Unlike banks, whose social function is to transform riskand maturity, CCPs are not in the business of taking risk directly but of pooling risk.Therefore, we do not want to regulate them primarily as risk takers. Instead, our aimis to control the way they propagate risk, including in particular the way they allocatelosses in case of a participant’s default.

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A CCP is a CCP is a CCP 3

resilience of systemically important CCPs and other market infrastructures is nowconsidered essential for maintaining financial stability.

This has led to an appropriate level of interest among financial regulators in theresilience of these institutions, resulting in various efforts to strengthen the regula-tory standards that apply to CCPs. Notable among these efforts were the Principlesfor Financial Market Infrastructures (PFMIs) issued by the Committee on Paymentand Settlement Systems–Technical Committee of the International Organization ofSecurities Commissions (CPSS–IOSCO) in 2012.2 The PFMIs, along with later pol-icy guidance including CPMI–IOSCO (2017) and Financial Stability Board (2017),provide international guidelines for the national regulators who oversee CCPs.

The systemic significance of CCPs has created a challenge for many in the reg-ulatory community. In the search for a framework to analyze CCP risk, there hasbeen a natural tendency to look for analogues in the shape of more familiar elementsof the financial system. Accordingly, CCPs have been seen as akin to payment sys-tems, central depositories and other financial institutions, while the default waterfallthat is characteristic of a CCP has been compared with collateralized debt obliga-tions (CDOs) (see, for example, Murphy and Nahai-Williamson 2014; Gregory 2014,p. 273). Given their systemic significance and the fact that they house significant risk,there has been an even greater tendency to view them as banks.

While there are certain aspects and risks that CCPs share with all of these elementsof the financial system, CCPs must ultimately be considered as a class apart from allof them. An especially significant mistake is to view CCPs as banks, although this isan understandable misconception given that CCPs in some jurisdictions have bankinglicenses.

Capital regulations are one area in which a banking framework is currently beingapplied to CCPs. The latest draft of the EU Capital Requirements Regulation appliesa binding leverage ratio and a net stable funding ratio to CCPs, in spite of the factthat these capital and liquidity safeguards were developed to protect against the risksbeing faced by banks, not CCPs (Wilkes 2017).

Like capital regulations, CCP resolution guidance imports concepts from banking.One such concept is the no-creditor-worse-off (NCWO) principle. The NCWO princi-ple requires that a bank has the resolution authority to ensure its actions leave no bankcreditor worse off than they would have been had the bank simply been liquidated.Financial Stability Board (FSB) guidance (2017) seeks to apply this principle to CCPresolution. International guidance also contemplates using a bridge institution duringthe process of CCP resolution. This notion of a bridge institution was developed for

2 The PFMIs were originally adopted by the Committee on Payment and Settlement Systems (CPSS),the predecessor of the Committee on Payments and Market Infrastructures (CPMI).

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banking resolution and allows a resolution authority to separate out a bank’s goodand bad assets (Financial Stability Board 2017).

We recognize, of course, that no regulator or policy maker would argue that CCPsare banks and should be regulated as such. However, there has been a tendency amongCCP regulators and policy makers to use methodologies developed in response to thefinancial crisis of 2008–9. The application of such methodologies, developed forissues in banking, may well miss the fundamental difference between the risk profileof a CCP and that of a bank.

3 CENTRAL COUNTERPARTIES: A DEFINITION

CCPs are commitment mechanisms (see Box 1). Understanding that CCPs are insti-tutions intended to assure commitment is critical in recognizing that, unlike banks,they are not in the business of risk taking (Duffy et al 2015, p. 3). To be sure, riskis transformed, rather than eliminated, through central clearing, and a CCP – as thesubstituted counterparty to all trades accepted for clearing – plays an important rolein managing the associated credit, liquidity, operational and other risks, as well asproviding a mechanism for market entry/exit that supports liquidity. However, theprimary function of a CCP is to ensure that clearing members meet their obligationsin accordance with the CCP’s rules. This function is fundamentally different from therisk-taking functions performed by depository or credit institutions.

CCPs developed from the evolution of clearing houses, with counterparty substi-tution introduced in the late nineteenth century. This was in response to the resolve ofcommercial interests to find a means of risk management specific to the trading risksarising in connection with commodity markets (Norman 2011; Steigerwald 2015).Clearing houses essentially developed as “clubs”, in which members jointly con-tributed to the management of risk, ultimately culminating in the modern formula ofclearing member “loss mutualization” (Cox and Steigerwald 2016).

Although this evolution was based, in part, on models drawn from payment clear-ing arrangements, it was a distinct development that took place outside of ordinarybanking practice. Further, it was not initially supported by any explicit law or publicpolicy (Moser 1998; Steigerwald 2015). Rather, it was supported by the rules of theclearing associations themselves. These rule structures were based on the “collectiveresponsibility” of clearing members as well as a set of established membership stan-dards and disciplinary mechanisms, including expulsion, to enforce commitment tothe rules.

Why does this matter? A failure to understand that clearing is about commitment,not asset/liability (maturity) transformation, and that CCPs are not banks leads tothe application of an inappropriate paradigm for regulating CCPs; one that viewsCCPs through the lens of banking regulation and, in particular, capital regulation. As






BOX 1 What does it mean to say that a CCP is a “commitment mechanism”?

A CCP can best be understood as an institutional structure intended to ensure commit-ment for market participants (Nosal and Steigerwald 2010; Monnet and Nellen 2012). Theeconomic purpose of the CCP’s guarantee of performance is “to enable investors to tradewithout concern about the creditworthiness of the individuals with whom they are deal-ing” (Bernanke 1990, p. 136; Greenwald and Stiglitz 1992, pp. 4–5). As in other contextsthroughout history, commitment must be institutionalized to provide real “security ratherthan mere promises” (Greif et al 1994, pp. 754–755).

To be sure, CCPs facilitate multilateral netting, collateralization and loss mutualization,while allowing market participants to enter into (and exit) market positions more easily(Steigerwald 2015, pp. 213, 226, 229). All of these functions support and reinforce tradercommitment.

A stable, coherent body of law (contract, property or insolvency, for instance) may alsocontribute to commitment and is essential in the proper functioning of the financial system.However, there are limits to the effectiveness of law as a commitment mechanism. Thespecial circumstances under which “complete” clearing evolved in North America in thelate nineteenth and early twentieth centuries necessitated a new, private institution – oneable to support trader commitment – since existing law was insufficient (Stassen 1982,p. 826).

A CCP provides a credible, if imperfect, guarantee that financial obligations will be met.CCPs provide “the convenience, safety and trust required for national and global [financial]markets to function” (Green 2001, pp. 115–116). These and similar institutions are neces-sary because some welfare-enhancing transactions would not take place in the absenceof commitment (Nosal and Steigerwald 2010). To understand a CCP as a whole, ratherthan as the sum of its parts, we must understand that it is an institutional structure thatsupports credible commitment.

discussed in this paper, CCPs are not banks, and CCP capital plays a subordinate yetcomplementary role in CCP risk management.

4 WHY CENTRAL COUNTERPARTIES ARE NOT BANKS

I tell ’em that this country is bigger than Wall Street, and if they don’t believe it, Ishow ’em the map.

Will Rogers (1929)

On a fundamental level, the difference between CCPs and banks begins with theirbusiness models. While both serve an intermediary function, the similarity ends there.The contrasting business models in turn drive a strong divergence in their risk profiles,which at times appear as inverse images of each other. It is this marked difference inthe risk profiles that poses the greatest challenge to those who would evaluate CCPsusing methodology borrowed from bank risk management.






4.1 Business models

Banks are risk takers. In their simplest form, they intermediate deposits and short-termfunding against longer-term credit provision. As a consequence, they run mismatchedbooks: their assets from credit provision do not match their liabilities to their sources offunding. This inherent mismatch is supported by capital, and much of the regulatoryeffort made to promote bank resilience is focused on determining the appropriatestandards of capital adequacy.

CCPs are risk managers. They serve as substituted counterparties to both sides of atransaction brought to them by their clearing members, becoming the sole principal toboth buyer and seller after a transaction has been consummated and cleared (Tucker2011, pp. 1–2). In contrast to the mismatched books run by banks, CCPs run matchedbooks. CCPs bear counterparty risk, that is, the risk that a clearing member will failto meet its obligations. Only if a clearing member fails to meet its obligations will aCCP face market risk. A CCP bears this risk until the defaulting member’s positionsare either liquidated or transferred to another member. CCPs collect margins andmutualized default resources from clearing members to support their temporarilymismatched exposure after a clearing member default.

As noted above, CCPs become the counterparty to both sides of a transaction. Theydo so through a process known as novation (or an equivalent legal mechanism). Whentwo market participants bring a contract to the CCP for clearing, the CCP terminatesthe original contract and substitutes two new contracts, both of which are betweenthe CCP and one of the original counterparties. The use of novation turns the CCPinto a principal to the trades it clears.

Some of the academic and policy literature continues to consider the clearing houseas a mere agent of the original counterparties to cleared trades. This incompleterecognition of the implications of counterparty substitution results in a conception ofthe CCP as an insurer, rather than a principal to cleared trades. The clearing memberswho originate cleared trades do not continue in a bilateral contractual relationship withtheir trade counterparties once these trades have been accepted for clearing. They arereplaced by the CCP as a substituted principal for both sides of those trades and retainan interest in their former counterparty’s performance only insofar as they remainresponsible for mutualizing losses in the event of that clearing member’s default.

A related misunderstanding treats CCP clearing primarily as an insurance function.3

Loss mutualization among the clearing members of a CCP is, of course, a core featureof CCP clearing, and it is a common means by which members of a clearing house (and

3 See, for example, Koeppl and Monnet (2010, p. 3) and Fontaine et al (2012, p. 15), who statethat “risk sharing in the CCP provides benefits from diversification similar to those created by aninsurance company”.






other membership associations) provide “self-insurance” for their activities. But CCPclearing does not employ the usual mechanisms of insurance, namely, risk poolingand diversification.

4.2 Risk profiles

Both banks and CCPs face significant risks: credit, liquidity, operational and systemic(Hughes and Manning 2015, p. 73; Hughes and Manning 2016, p. 14; Lin and Surti2013, p. 5). The sources of these risks, however, differ between CCPs and banks.

In their more sophisticated form, banks are involved in multiple lines of risk tak-ing, including principal investing; securities underwriting; capital markets and trea-sury services; and securities, derivatives, foreign exchange, energy and commoditiesmarket making. All of these activities expose banks to a wide range of counterpartyrisk, and all require capital support. Banks, as noted, intermediate between short-termfunding and long-term credit provision, with exposures that can extend over decades.CCPs may clear a wide or narrow range of market products. They have credit expo-sures only when a member defaults and, with a rigorous margin regime, can measureexposure to clearing members in intraday or, at most, overnight terms. Their riskresources are primarily a mixture of contributions from members. Initial margin isrequired from all members to support positions. At least daily, and sometimes onan intraday basis, variation margin is required from all clearing members holdingpositions with adverse marks-to-market. The margin regime, in turn, is backstoppedby a default fund provided by all clearing members and, in some cases, unfundedassessments provided by nondefaulting clearing members. This mutualization of riskby the clearing members may also be enhanced by contributions of CCP capital inthe case of a demutualized CCP.

Governance of bank risk taking is formal, with key oversight of the nature of therisk and its magnitude being undertaken by the board of directors on a principal basis.A major driver of this is the consideration of risk versus return (see, for example, USGovernment Accountability Office 1998, pp. 73–74).

Governance of CCP risk is fundamentally different from that of banks, as it is sharedbetween the CCP and its clearing members. Because risk is mutualized, both clearingmembers and the CCP participate in risk-related decisions, usually in the form of arisk committee that includes representatives of various members. Mutualization ofrisk is indeed a key distinguishing feature of CCP risk management as opposed tobank risk management.

Banks face liquidity risk when their immediate obligations to pay cannot be metby the liquidation of assets, a possible consequence of intermediating between short-term funding and long-term credit provision. Central banks have long recognized






this potential problem and stand as “lenders of last resort” against good collateral toprovide banks with sufficient liquidity.

CCPs face liquidity risk in the event of a member default since the CCP still hasan obligation to pay variation margin to nondefaulters in cash. Many CCPs havecommitted liquidity facilities that allow them to convert securities held as margin ordefault fund investments into cash, though these carry a possible element of wrong-way risk in cases where the failed member is in the liquidity facility. Some centralbanks provide credit access for CCPs. A clearing member’s failure to pay will causea default at the CCP; the CCP’s inability to pay its members can result in the defaultof the CCP. Arguably, this may pose the greatest risk to a CCP: even if it has adequatemargin collateral from the defaulter and other mutualized resources, the CCP maynot be able to make timely payments to surviving clearing members as a result ofsystemic liquidity issues.

Banks and CCPs both face liquidity constraints and the risk of insolvency. Forbanks, the mismatch between assets and liabilities can mask solvency, as the value andliquidity of longer-dated credit assets can be difficult to ascertain or realize. This canbe ameliorated by central bank liquidity provision (secured by good collateral).Accessto central bank liquidity may allow the bank to continue to operate, unless authoritiesdetermine that the bank is insolvent and place it in resolution. Bank insolvency isusually determined by assessing the bank’s balance sheet condition and/or ability tomeet ongoing obligations.

As noted above, CCPs also face liquidity risk in the event of a member default.However, solvency risk for CCPs is an extreme tail event because insolvency does notnecessarily arise from a member default. Of course, the failure to reestablish a matchedbook following a default may result in the depletion of a CCP’s default managementresources of margin collateral, default fund and assessments. These resources may beoverwhelmed as the CCP attempts to meet its obligations to pay surviving clearingmembers. Nevertheless, CCPs typically have rule-based recovery powers that allowthem to respond as needed to particular circumstances without impairing their balancesheets and without defaulting on their ongoing obligations to members or their generalcreditors.

While the main risks facing CCPs result from a clearing member default, CCPs alsoface nondefault risks. These fall into three broad categories: operational risks (cyberattacks, fraud, etc), investment risk (the risk that the instruments held as margin willfall in value) and custodial risk (the risk that a custodian used by the CCP will fail).International guidelines require CCPs to hold capital against general business lossesand set aside resources to cover custody and investment losses (CPMI–IOSCO 2017).Banks are also exposed to many of these risks and hold capital to protect against them.

In addition to facing risks, CCPs and banks can also create risk. Many larger banksand CCPs are systemically important. As highly interconnected financial institutions,






their failures could have significant negative effects across the entire financial system.This similarity means that both CCPs and banks must be regulated effectively; thissimilarity does not dictate the particular regulatory regime that will be most effectivefor each type of institution.

4.3 Capital and collateral in periods of stress

Further distinctions between banks and CCPs can be observed in the respective rolesand effects of capital and collateral.

Before we begin, we wish to make a distinction between initial margin and collat-eral. Initial margin is a requirement, rigorously calculated by CCPs, to be held againsta member’s performance obligations for changes in value of the positions they hold.Collateral is the instrument posted at the CCP to meet the initial margin requirement,typically high-quality liquid securities or cash.

CCP initial margin requirements are determined largely by market risk consider-ations, including price volatility, concentration and specific market liquidity. Thereis no consideration of the credit ratings of either the clearing members or their cus-tomers in determining the base margin levels. Base margin levels (before concentra-tion and other risk add-ons) are applied uniformly to all members. The creditworthi-ness of a clearing member only becomes an issue if it no longer meets membershiprequirements.

Variation margin at CCPs is paid in cash and reflects changes in the position valuesbased on updated marks-to-market. Collected from those who have lost due to thenew marks, variation margin is, in turn, paid by the CCP with finality to those whohave won. Failure to pay variation margin is the most likely cause of a member’sdefault and highlights an important liquidity risk faced by all members.

As previously noted, CCPs run matched books, reflecting the perfect synchroniza-tion of “long” and “short” positions cleared by the CCP. The matched book is notreflected on the CCP’s balance sheet. It is a reflection of the principal risk posi-tion undertaken by the CCP as the substituted counterparty to all trades accepted forclearing.

A CCP’s book will become unmatched and the CCP will face market risk in theevent of a clearing member default. The defaulting member’s margin collateral andmutualized resources are available to the CCP to be used as necessary, and the CCPwill take steps to restore the book to its matched state. This has no capital impacton the CCP because the matched book is not generally carried on a CCP’s balancesheet. In jurisdictions where defaulting members’ collateral or the CCP’s mutualizedfinancial resources are reflected on a CCP’s balance sheet, appropriate balance sheetentries will, of course, be made to reflect the use of these resources. However, because






these entries will balance with assets used to meet liabilities, they will have no effecton the CCP’s capital.

In the case of banks, capital supports their mismatched books. Capital is essentiallya balance sheet concept. Capital adequacy requirements are imposed on banks andvarious other financial institutions to provide a buffer against loss, to protect creditorsin the event of a bank’s failure, and for related regulatory purposes (Tarullo 2008,pp. 16–29; Berger et al 1995). This generally means that equity and other juniorclaims on a bank are residual claims, available to support the ongoing operationsof the bank and to meet creditor claims should the bank be forced into resolution.Moreover, a bank’s capital is “usually difficult to redeem during a financial crisis,mitigating systemic risk problems and buying time for regulators to deal with thecrisis” (Berger et al 1995, p. 30). Bank capital, however, is not a liquid asset to beliquidated in the event of distress (indeed, capital appears on the liability side of abank’s balance sheet).

For example, a bank may become distressed as a result of large portfolios of non-performing loans. Under applicable accounting and regulatory requirements, the bankmay be required to write down the balance sheet value of these assets. As a result,the bank must also write down the amount of its equity capital, the residual claim towhich shareholders are entitled. The bank’s balance sheet is thus restored to a balance,meaning that the bank’s total assets equal the sum of its liabilities. The bank may alsosell illiquid assets (to raise more liquid assets) or raise new capital to improve itscapital position. The resulting changes in the mix of the bank’s assets, liabilities andcapital will be reflected on the bank’s balance sheet.

A bank’s balance sheet is an essential source of information for the bank’s creditorsand other parties seeking an insight into the bank’s financial condition. In particular,it provides an indication to creditors and regulators alike of the bank’s ability to meetits ongoing obligations as they fall due. Moreover, it signals whether or not there is asufficient capital buffer that may be used to meet the claims of senior creditors in theevent of the bank’s failure.

A CCP’s balance sheet does not, and cannot, perform these functions. Critically,CCPs have obligations to their clearing members, who are direct contract counterpar-ties to the CCP. They care about the CCP’s ability to meet its obligations to them asprincipal to those obligations. As long as the CCP maintains a matched book, therecan be no doubt as to its ability to meet its obligations to clearing members, leavingaside the possibility of nondefault sources of distress, which we discussed brieflyabove.

The use of margin collateral and other resources (as opposed to CCP capital) inmanaging default is inextricably tied to the nature of the risk faced by a CCP as well asthe CCP’s function as a commitment mechanism. This has important implications for






the incentives of the CCP (and its owners and managers) and the clearing members.This effect may be subtle and will depend on the circumstances.

CCPs have established contractual mechanisms in the form of margin requirementsand mutualized resources, with which they may manage member defaults. Thesemechanisms have been carefully thought out and have evolved over more than a cen-tury of experience of CCP clearing on a global basis. They have generally performedvery well and execute risk management functions, including incentive alignment, thatcapital alone cannot (see, for example, Kroszner 2006, p. 37; 2009). The few situa-tions in which CCPs have failed to perform well did not meet current internationalstandards for effective risk management (Cox 2015b; Norman 2011).

5 POLICY IMPLICATIONS

The unique structure and function of CCPs have implications for the design of CCPregulation. In this section, we discuss three areas of concern that are relevant topolicy makers for both banks and CCPs. These are capital, risk profiles under stressand resolution. For each issue, we note how the specific needs of banks and CCPsdiffer.

5.1 Capital

CCP capital cannot be the primary resource (or even a significant one) for loss absorp-tion without fundamentally altering the incentive structure embedded in the defaultwaterfall. As Cœuré (2015) points out, CCP capital would need to be dramaticallyincreased from current levels “in order to represent any meaningful part of the defaultwaterfall”. This, in turn, would “completely chang[e] the size of CCP balance sheetsand their business models”.

Altering the amount of CCP capital to take a significant role in absorbing defaultlosses, or altering its placement in the default waterfall structure, would change theincentives of the parties involved. Were the CCP capital in the waterfall to becomesignificant, it would come at the expense of diluting the mutualized risk characteristicof the CCP, which could provide a disincentive to members to support the neces-sary process of liquidating a failed member’s positions. This, in turn, would makesuccessful default management more difficult (Carter and Garner 2015; Cox 2015a).

Since CCP capital is not the primary resource for loss absorption, capital analysisalone tells us little or nothing about the resilience of a given CCP. Total loss absorbingcapacity (TLAC), a concept developed by bank regulators in the wake of the finan-cial crisis, is a largely capital-based requirement. TLAC is different from CCP lossabsorbing capacity (Gracie 2015; CPMI–IOSCO 2017; Basel Committee on BankingSupervision 2015c). Default loss absorption is relevant to CCP resilience and must be






measured according to the total of all financial resources available to the CCP, whichcritically includes defaulter collateral as the primary resource.

There is no simple analogy to be drawn between a CCP and a bank with respect tothe roles of capital and collateral: they serve different purposes. Banks use their owncapital and any collateral pledged by borrowers for the purposes of asset and liabilitymaturity transformation. CCPs primarily use clearing member collateral (both in theform of margin and default fund contributions), as well as a prespecified amount ofthe CCP’s capital, to assure commitment and provide for the mutualization of anytail losses by clearing members. TLAC tells us about bank risk and the adequacy ofcapital to absorb that risk; however, different information is needed to identify risksand measure the adequacy of resources for the management of CCP risk.

5.2 Contrasting risk profiles under stress

Default management is the process by which a CCP handles a member’s default.This will involve the liquidation of the member’s positions in order to reestablisha matched book and the transference of customer positions and collateral, providedwilling nondefaulting members can be found to accommodate this. The CCP fundsthe payments of variation margin due to the nondefaulting members, ideally usingthe resources (that is, the margin and the default fund contribution) of the defaultingmember. Should these prove inadequate, the remaining default management resources– comprising the CCP’s committed capital, the default fund and, if necessary, assess-ment powers – will be used to cover any shortfall. CCP recovery is a rules-basedprocess that begins only after default management fails and the prefunded resourcesof the CCP prove insufficient.4

The risk profile of a bank under stress is different. The risk management andrecovery tools used by CCPs are not available to banks. For example, a bank mustdeal with a nonperforming loan in accordance with the terms and conditions underwhich that loan was extended. If the borrower is subject to bankruptcy proceedings,these contract rights will be subject to the applicable insolvency law. Moreover, therights of depositors cannot be unilaterally terminated by a bank that is insolvent. Thisfeature of the basic demand deposit contract is crucial to understanding bank runs. Abank facing a run may suspend depositor withdrawals, but may not alter its obligationsto depositors. Aside from the protection afforded by deposit insurance, any alterationof depositor claims must take place in the resolution process. The bank’s creditorsare dependent ultimately on the assets of the bank that remain after satisfying itsliabilities. These assets are the source of any recovery with respect to their claims.

4 CPMI–IOSCO (2014, p. 3) notes that recovery “concerns the ability of an FMI [Financial MarketInfrastructure] to recover from a threat to its viability and financial strength so that it can continueto provide its critical services without requiring the use of resolution powers by authorities”.






There are, however, some similarities between “bail-in” for the purposes of bankrecovery and typical CCP default management and recovery practices. For example,the basic objectives of both CCP and bank recovery are similar (Hughes and Manning2015, p. 76; Hughes and Manning 2016, p. 20). Moreover, Cœuré (2015) notes that“the concept of ‘bail-in’is directly built into [CCP] risk management” and “is achievedto a very large extent via prefunded resources provided by the clearing membersthemselves”.

The process by which clearing members are contractually obliged to participatein CCP recovery, however, is completely different from the conversion of debt toequity that may facilitate the recovery of a bank. As Tucker (2011, p. 3) points out,“a distressed bank gets ‘recapitalized’ in some way through a reconstruction of itsliabilities”. Unlike banks, “CCPs do not issue debt, so there is not obviously aneconomic equivalent of recapitalization by way of haircutting the debt claims ofbondholders”. Further, Duffie (2013, p. 257) states that CCPs do not have a “large classof unsecured creditors to absorb losses”. Accordingly, Cœuré (2015) concludes that“[t]he statutory tools implemented by banking supervisors for ensuring the resilienceof banks may … not be appropriate for CCPs, and we should be cautious about anycomparisons we make. They could be misleading.”

The fact that CCPs have extraordinary default management and recovery powersis widely recognized as an internal, private-sector mechanism (Duffie 2015). There isno similar process outside of bankruptcy or resolution by which banks may managetheir obligations to creditors.

5.3 CCP resolution compared with bank resolution

Bankruptcy is a means of market exit for underperforming business enterprises, butthis process involves externalities for banks and other systemically important financialinstitutions. As a result of their unavoidable exposure to liquidity risk, policy makerstoday generally consider banks to be special and unlike ordinary business firms.For example, policy makers have developed various “safety nets”, such as depositinsurance, in an attempt to limit the systemic consequences of bank liquidity crises.Similarly, insolvent banks, unlike ordinary corporations and other business entities,are subject to resolution under procedures that are generally designed to deal withboth creditors’ rights and the systemic consequences of insolvency.

The prevailing political dogma of the G20 leaders following the global financialcrisis demonstrates that policy makers will allow systemically important financialinstitutions, including banks, to be wound up if they are insolvent and a private sectorsolution cannot be found. In the contemporary political environment, no bank or CCPis “too big to fail”. However, as explained above, CCPs do not necessarily becomeinsolvent as a result of a member default. CCPs have rule-based recovery powers that






they can use to reestablish a matched book. Those powers are directed at preservingthe continuity of the open contracts cleared by the CCP, thus serving the interestsof the CCP’s members, their customers and the public at large. Consequently, policymakers have expressed a clear preference for maintaining the continuity of serviceprovided by a CCP in trouble. Moreover, this preference is not inconsistent with thepolitical imperative to eliminate the expectation that some institutions are “too big tofail”.

Banks, being risk takers by nature, are vulnerable to failure, especially in periodsof high stress. There were 165 bank failures in the United States during 2008 and2009.

CCPs, being risk managers, can fail, but failure is rare. There have been three since1974, in Paris, Kuala Lumpur and Hong Kong, the most recent of which occurredin 1987. There have been none in the United States. Modern CCP risk managementpractices and the mutualization of risk by members were largely absent from thesethree failures. In general, CCPs had evolved to a reasonable degree of resilience wellbefore the circulation of the PFMIs in 2012.

Consistent with international policy practice, we define CCP resolution as the entryof an external authority, with legal authorization, into the process of managing a failingCCP. Some resolution authorities may enter before recovery is completed in orderto utilize the dwindling resources available to the CCP. A failed (or failing) recoverywould be indicative of surviving clearing members’ inability (or unwillingness) towork with the CCP’s management to resolve the crisis. This would most likely beaccompanied by the persistence of an unmatched book due to the CCP’s inabilityto liquidate the failed member’s positions. While the use of recovery tools fromthe CPMI–IOSCO recovery toolbox may be contemplated, it is the presence of theexternal authority that distinguishes resolution from recovery.

CCP resolution can have different outcomes. One might be the temporary main-tenance of market function to allow for an orderly wind-down of positions. Anotherpossible outcome could be the rehabilitation of the CCP in order to assure continuationof service.

The most important factor in CCP resilience may well be the resilience of a CCP’sbank members, since the event most likely to trigger a crisis at a CCP is a bankclearing member default. Banking regulators may wish to take this into account whenmanaging a bank resolution; it may be in the best interests of a bank resolutionauthority to preserve the failing bank’s positions at the CCP (likely to be hedges),thus helping to manage the failing bank’s overall risk in resolution more smoothlyand to prevent the bank failure from leading to trouble at a CCP. To do so would meanensuring that the failing clearing member does not default at the CCP.

A final important difference between bank and CCP resolution concerns the appli-cation of the NCWO principle. Recent FSB guidance seeks to apply this principle






to CCP resolution (Financial Stability Board 2017). The NCWO principle, however,is unworkable in the context of CCP resolution. In bank resolution there is a naturalcounterfactual – the proceeds that would result from liquidation of the bank’s assets– with which to measure whether or not a creditor has been made worse off. CCPresolution allows for no such clear counterfactual; any action taken by a CCP or itsresolution authority during recovery and resolution will artificially create winnersand losers. This deficiency is not remedied by assuming “the full application of theCCP’s rules and arrangements for loss allocation” as the counterfactual for the pur-poses of the NCWO principle (Financial Stability Board 2017, p. 10). The rules ofa CCP provide alternative mechanisms for restoring a matched book and allocatinglosses; the application of these rules involves decisions regarding price and timingthat will necessarily affect different clearing members differently. Further, unlike inthe banking context, it is not clear who should be considered a “CCP creditor” for thepurposes of applying the NCWO principle.

6 CONCLUSION

CCPs have unique risk profiles compared with other market infrastructures and banks.Importantly, CCPs are risk managers rather than risk takers. In contrast to banks, CCPcapital does not constitute a significant resource for default management. Rather,a combination of initial margin collateral and mutualized resources from clearingmembers is the primary support for a CCP’s loss absorption capacity in the eventof member default. CCPs also have unique default management and recovery toolsthat are not available to banks. For public policy makers addressing issues of CCPrisk management and resilience, the challenge is to construct policy that accuratelyincorporates the unique features of CCP risk profiles.

DECLARATION OF INTEREST

The views expressed in this paper are the authors’ own and do not reflect the policyof the Federal Reserve Bank of Chicago or the Federal Reserve Board. The authorsreport no conflicts of interest. The authors alone are responsible for the content andwriting of the paper. An earlier version of this paper appeared in Central BankingJournal 27(4), May 2017, pp. 60–71. See https://bit.ly/2IF86x5.

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Research Paper

Freeriding on liquidity in the Colombianlarge-value payment system

Constanza Martínez and Freddy Cepeda

Financial Infrastructure Oversight Department, Banco de la República, Carrera 7, 14–78, Bogotá,Colombia; emails: [email protected], [email protected]

(Received August 16, 2017; accepted April 11, 2018)

ABSTRACT

The functioning of a large-value payment system (LVPS) can be affected when someof its participants intentionally decide to delay their payments until they can fundthem with payments received from other participants. This payment strategy, knownas the free rider (FR) problem, can cause under provision of liquidity in LVPSsoperating in real-time gross settlement (RTGS) mode. We empirically tested for theexistence of FRs in the Colombian LVPS (CUD) between November 2014 and April2016. This entailed using regression techniques (quantile regression models) and thencomputing the empirical quantiles. Our results provide evidence of this problem inthe CUD; however, its negative effect on the payment system is considered small.

Keywords: payment system; freeriding on liquidity; liquidity hoarding; quantile regression models;liquidity provision.

1 INTRODUCTION

The payments made by a financial institution and settled through a large-value pay-ment system (LVPS) can be funded using loans (from the central bank and the moneymarket), the institution’s own balance at the central bank, and/or payments received

Corresponding author: C. Martínez Print ISSN 2049-5404 j Online ISSN 2049-5412© 2018 Infopro Digital Risk (IP) Limited







20 C. Martínez and F. Cepeda

from other system participants (McAndrews and Potter 2002). Under normal circum-stances (that is, when interest rates are greater than zero), the only source of liquidityavailable free of charge is that represented by the payments received.1 Money marketinstruments, such as repurchase agreements (repos) with the central bank or withother banks, are priced at interest rates set by monetary policy in the first case, andmarket dynamics in the latter case. But a repo, whether with the central bank or in thesecured money market, involves an additional opportunity cost for posting collateral.

As noted byAngelini (1998), when intraday liquidity is costly it creates an incentivefor banks to free ride on the liquidity provided by others; this involves postponingtheir payments until they can be fully funded with payments collected from othersystem participants. From an individual perspective (that of a system participant),freeriding may be considered an optimal strategy for payment because the cost ofliquidity is cut to zero. However, from a wider perspective it may not be optimal, asit can reduce the system’s effectiveness in avoiding liquidity risk.2

Certain empirical studies on this topic have found evidence of financial institutionsthat under provide liquidity (free riders (FR)) in LVPSs (see Denbee et al (2012,2015) for the United Kingdom’s LVPS and Diehl (2013) for the German LVPS) aswell as financial institutions that over provide liquidity (Denbee 2015). When a systemparticipant has to send a payment but lacks any other source of funding, freeriding onothers’ liquidity is the only way to fulfil its payment obligations. When other liquiditysources are available, however, freeriding evidences liquidity hoarding. In view ofthe fact that the under provision of liquidity of FRs may threaten the functioningof payment systems, a formal evaluation of this payment strategy may be usefulfor oversight purposes as well as informative of how system participants are reallyfunding their payments, and when additional rules on liquidity management might berequired. Hence, the study of this topic will allow LVPS owners and managers (suchas central banks) to identify and monitor the financial institutions that are following

1 Interest rates lower than zero could change the preferential ordering of liquidity sources, makingsome of them cheaper than incoming payments. This has never been the case in Colombia, in thesense that negative policy rates (or a rate that is equal to zero) have not yet been observed. In othercountries, such as Japan and the eurozone, negative interest rates have been set, via policy measures,by central banks on overnight deposits to stimulate economic recovery in particular periods. TheEuropean Central Bank (ECB) reduced the depositary facility rate from �0.1% in June 2014 to�0.4% in March 2016. The Central Bank of Japan applies an interest rate of �0.1% to currentaccounts that financial institutions hold at the bank (see the statistics published by the ECB and theBank of Japan).2 We assume the definition of “liquidity risk” provided by CPSS–IOSCO (2012), since this isexclusively related to the risk of a financial institution not settling an obligation of payment in fullwhen it is due.





Freeriding on liquidity 21

this payment strategy and determine the extent to which it can represent a problemfor the safe and efficient functioning of the payment system.

A recent assessment of payment reaction functions in some of the financial institu-tions that use the Colombian LVPS (CUD) to process their funds transfers found thatthere is some coordination in the sending of payments, but that the degree of suchcoordination is rather low (Martínez and Cepeda 2015). These findings, along withthe levels of liquidity recycling within various system participants (during the firsthalf of 2015, the percentage participation of incoming payments within the fundingsources was beyond 40%; see Banco de la República (2016)), advocate for a formalevaluation of the FR problem in the context of large-value payments. To this end,we compute five measures of liquidity provision (proposed by Denbee et al (2012)and Diehl (2013)) using simulated days (constructed around random rearrangementsof registered payments) to test the FR problem using quantile regression models andempirical quantiles. The former methodology has been used by Denbee et al (2012),and the latter by Denbee et al (2015). Quantile regression results depend on the modelspecified, while the results obtained with empirical quantiles are determined only bythe data used. In both approaches, the fifth percentile of the empirical distributionfunction of the data is used as a 5% confidence threshold to determine whether or nota financial institution is using the freeriding strategy. The results obtained from thesemethodologies coincide in that they both evidence the existence of FRs on liquidityin CUD and show that the degree of freeriding is small.

2 FREE RIDERS IN LARGE-VALUE PAYMENT SYSTEMS

A financial institution’s decision on which source of funding to use when makinga payment essentially depends on how costly that source is compared with otheralternatives. Financial institutions can fund their payments using their own liquidityheld in accounts with the central bank, or the liquidity collected from counterparties,but they can also resort to the central bank or to other system participants. The costof liquidity in either of the last two cases is given by the interest rate charged bythe lender, but it may also depend on the existence of collateral requirements thatapply to repos both with the central bank and with other financial institutions. For afinancial institution looking to raise funds, the posting of collateral may be consideredan additional cost (an opportunity cost in economic terms) that can make a fundingsource even more expensive. Among these funding sources, reusing (recycling) thepayments received from counterparties seems to be preferred above the others; this isnot surprising, since it is the only source that involves no cost for its users. However,following this payment strategy, even when other funding sources are available, iswhat defines the FR problem. As noted by Angelini (1998), when the cost of daylightliquidity is high, system participants may feel tempted to postpone their payments.






Hence, following the FR strategy may imply engaging in payment delays that canaffect the normal functioning of the payment system.

Freeriding behavior is considered intentional when a payment delay arises as astrategic decision; however, it may also be unintentional when it emerges from thetype of business conducted by a financial institution (Denbee et al 2012). In any case,as noted by Becher et al (2008), delaying payments could imply additional costs forany financial institution following this payment strategy, in the form of

� financial penalties, which may be incurred by not sending time-critical pay-ments at the agreed time;

� the reputational cost of delaying payments made on behalf of clients; and

� other reputational costs, which may be incurred when other system participantsdecide, as a punishment strategy, to stop sending payments to the financialinstitution that is being perceived as an FR.

Therefore, individual decisions regarding the timely release of payments (that is,cooperating by sending payments) – as opposed to their postponement – depend onthe relative cost of liquidity; but they may also depend on the cost of delaying them.However, whatever the source of the delay in payment, the cost of delay supposes,to some extent, the institution to be in possession of complete (perfect) informationabout its individual payments. As Becher et al (2008) indicate, system participantsfrequently lack full information on their payment flows.

The existence of participants behaving as FRs does not represent a problem fora payment system per se, but it could turn into a problem when it reaches a levelthat affects the system’s efficiency. In fact, a payment delay will not only reduce theexpected cost of liquidity for the FR, but also increase the cost of liquidity for thefinancial institution expecting the funds. As a result, it may produce a deadweightloss to the system relative to the cooperative outcome (Nellen 2011). That is why thistopic is of high importance for central banks that own and manage an LVPS operatingin real-time gross settlement (RTGS) mode, given its objectives of guaranteeing thesmooth functioning of payments and the efficient use of liquidity.

Previous studies on this issue are scarce. In the payments economics literature, thefreeriding problem has been considered tangentially, mostly focusing on the negativeconsequences that it could produce on payment systems.Angelini (1998) and Galbiatiand Soramäki (2010) concur that this behavior is one of the main causes of inefficiencyin LVPSs that transfer funds in RTGS mode, since it could generate an under provisionof liquidity to the system and, hence, lead to a reduction in the LVPS’s capacityfor avoiding liquidity risk. Likewise, as Galbiati and Soramäki (2010) indicate, theadverse effects that FRs may have on the liquidity of LVPS–RTGS systems can






be lessened, but not completely solved, by adopting liquidity-saving mechanisms(LSMs).

Other studies on this topic have focused on developing, from different angles, spe-cific measures that allow for the empirical assessment of the existence of FRs withinpayment systems. Denbee et al (2012) proposed two measures of liquidity provision,based on cost and risk criteria, but concluded from their results that neither measureallows us to separate intentional FRs from unintentional ones. To circumvent this lim-itation, they executed simulations of intraday payments, using them to compute theirtwo measures of liquidity provision; they then tested for the FR problem using quantileregression models (Denbee et al 2012) and empirical quantiles (Denbee et al 2015).Under both empirical assessments, their results confirmed the existence of FRs in theUK LVPS: the clearing house automated payment system (CHAPS). They authorsalso confirmed that the banks following this payment strategy have not affected liq-uidity usage in a considerable manner.3 A similar result was found for the Germancomponent of the European LVPS, namely, the trans-European automated real-timegross settlement express transfer system–Deutsche Bundesbank (TARGET2–BBK).Diehl (2013), using a set of axiomatic postulates on five measures of liquidity provi-sion (comprising three indicators in addition to the two measures proposed by Denbeeet al (2012)), found that although some banks have behaved as FRs, their effect onthis payment system has also been low.

3 DATA DESCRIPTION

The Colombian LVPS that processes the funds transfers between financial institutionsparticipating in the local financial markets is CUD, and it is owned and managed bythe Colombian Central Bank (Banco de la República). This payment system operatesin RTGS mode, enhanced by various LSMs (more precisely, algorithms that automat-ically process unsolved transactions, netting payments between system participants)that optimize the management of intraday liquidity (Martínez and Cepeda 2015). Theyearly volume of settled payments in 2016 represented 14.9 times the gross domes-tic product (GDP).4 Like all LVPSs operating in RTGS mode, this payment systemis more susceptible than other systems (DNS systems, for instance) to coordinationproblems in the sending of payments, potentially leading to situations in which a pay-ment delay can impede a huge number of other participants executing their pending

3 This result is also supported by the existence of throughput rules that may (at least partially)discourage financial institutions from adopting this payment strategy. These rules force all CHAPSparticipants to send 50% of payments by value by 12:00, and to complete 75% of payments as anaverage of the calendar month by 14:30 (Becher et al 2008).4 For a more detailed description of CUD, see Martínez and Cepeda (2015) and León et al (2011).






TABLE 1 The sizes of different types of financial institution (in terms of payments; allvalues shown are percentages).

Banks 59.8Brokerage firms 10.2Trust companies 10.6Financial corporations 6.5

Total 87.2

disbursements (Kahn and Roberds 2009). In CUD, all financial institutions and somespecial official entities are allowed to participate directly.

The empirical appraisal of liquidity provision in CUD is based on five measuresproposed in the related literature, which were computed using payment data fromfinancial institutions that explains the majority of payments sent through the system.The sample of financial institutions is composed of twenty-four banks, twenty-twobrokerage firms, twenty-eight trust companies and six financial corporations. Thebusiness type they conduct presents some differences. Banks and financial corpo-rations are responsible for financial intermediation services that consist of channel-ing funds from depositors to borrowers. However, while banks are oriented towardhouseholds and firms, financial corporations provide loans to industry participants.The business of the remaining financial institutions is related to investment: brokeragefirms trade securities, while trust companies construct investment portfolios that caninclude securities and other assets.5

For a typical month representing the sample period (that is, between Novem-ber 2014 and April 2016), these financial institutions jointly signify around 87.2%of the total value of payments registered per day in the CUD, in which banks arethe largest system participants (59.8%) and financial corporations are the smallest(6.5%). Brokerage firms and trust companies can be considered, in this context, asmid-sized participants, each one representing more than 10% of intraday payments.

The distribution of payments for the eighty financial institutions considered in thesample reveals some degree of concentration (see Figure 1).

The Lorenz curve, constructed using the total value of payments sent out dur-ing a typical month, indicates that only 10% of system participants executed 57.7%of their intraday payments (that is, the remaining 42.3% of payments come from90% of system participants). The Gini coefficient computed for this data is 75%,

5 See León et al (2011) for a detailed explanation of the type of business conducted by these financialinstitutions.






FIGURE 1 Lorenz curve (on intraday payments).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Financial institutions

Intr

aday

pay

men

ts

which also indicates some degree of payment concentration in just a few systemparticipants.

3.1 Measures of liquidity provision

The following description of the measures of liquidity provision is brief; we adviseany reader seeking an in-depth explanation of them to consult the original sources:Denbee et al (2012, 2015) for the cost and risks measures, and Diehl (2013) for thetime-based indicators and relative net sending indicator.

3.1.1 The cost-based measure

The cost-based measure calculates the individual cost of liquidity provision, basedon each financial institution’s share of the payment activity (Denbee et al 2015).Expressed another way, this criterion is determined by comparing the liquidity burdenand the liquidity usage on an individual basis. The liquidity burden (Ls

i ) depends onthe total liquidity provided by each system participant on day s (its net debit position,N s

i .t/), and it can be computed by subtracting the total amount of payments received(ys

i .t/) from the total amount of payments sent (xsi .t/) on that same day. Hence, the

cost-based measure for the i th financial institution will be given by the differencebetween its percentage participation in the total liquidity provision (largest net debitposition, Ls

i D maxt�Œ0;T � N si .t/) and the total liquidity usage:

�si D Ls

iPnj D1 Ls

j

� xsi .T /Pn

j D1 xsj .T /

: (3.1)






Consequently, a financial institution will be considered an FR when the liquidity thatit uses from the system (denoted by the second term in (3.1)) exceeds the liquiditythat it provides to it (�s

i < 0).

3.1.2 The risk-based measure

The risk-based measure assesses an individual’s exposure to the liquidity risk ofcounterparties. This depends on the difference between this individual’s share ofthe total risk (denoted by the first term in (3.2) that depends on the average risk,�s

i D .1=T /PT

tD0 maxŒ.xsi .t/ � ys

i .t//; 0�) and its share of the payments made onthat same day (Denbee et al 2015):

� si D �s

iPnj D1 �s

j

� xsi .T /Pn

j D1 xsj .T /

: (3.2)

Therefore, when the level of counterparty risk assumed by a financial institution isbelow the amount of liquidity that it uses from the system .� s

i < 0/, this measuresuggests a freeriding behavior, since the liquidity that it is providing to the system islower than expected (that is, it is under providing liquidity).

3.1.3 The time-based measure

The time-based measure determines the percentage of the day required by a financialinstitution to receive (send) half of its payments. For the assessment of this mea-sure, each business day is divided into the total number of minutes that exist withinthe thirteen hours in which the Colombian LVPS operates; this corresponds to 780time packages (thirteen hours times sixty minutes per hour). In addition to this adjust-ment, the time-based measure assumes that no difference exists between the paymentssent (received) in different seconds within the same minute; consequently, they areregarded as if they were registered in the same time package. Thus, this measuredepends on the difference between the average reception time index and the averagepayment time index:

ıi D�

average receptiontime index

�

i

��

average paymenttime index

�

i

: (3.3)

Based on the results obtained from this measure, a financial institution could beconsidered an FR when, according to Diehl (2013), it requires a larger share of theday to send half of its payments than to collect half of the payments coming fromother system participants (ıi < 0).






3.1.4 The early payment time indicator

Akin to the time-based measure, the early payment time indicator calculates the timeat which half of the total payments in value have been settled (see Diehl 2013).However, this indicator assigns a higher weight to the payments solved during theearly hours of a given day than to those solved in later hours, using a decay factor thatcontinuously decreases until it reaches a zero weight for the last time package of theday. The factor describing the weights will diminish consistently with a decay factorof 0.1% that determines a weighting factor per day .1:001780�t / � 1. This weightingfactor, which also depends on the number of time packages per day (780), is used tocalculate both the early payment indicator and the early reception indicator. The earlypayment (reception) indicator will be equal to 1 when a participant sends (receives)all its payments in the first time band of the day, or 0 if this occurs in the last timeband. Hence, this indicator can be considered as a weighted version of the time-basedmeasure, given by

�i D�

early receptionindicator

�

i

��

early paymentindicator

�

i

: (3.4)

Thus, a financial institution that under provides liquidity to the system (one that freerides) will correspond to a negative result of the criterion .�i < 0/ given by thosecases in which the early sending of payments exceeds the early reception of payments.

3.1.5 The relative net sending indicator

The relative net sending indicator completes the set of measures suggested by Diehl(2013). Like the measures based on cost and risk, this indicator depends on the largestnet debit position (Ls

i ) but also considers extreme situations in terms of liquidity (forinstance, a participant with large liquidity needs). More formally, this indicator isgiven by the difference between the ratio of the largest net debit position (Ls

i ) to thesum of all incoming payments (

PTtD0 xrec

t ) and the ratio of the largest accumulatedamount of net reception to the sum of all outgoing payments (

PTtD0 xsent

t ). Thisprocess may be expressed as

�i D LsiPT

tD0 xrect

� Abs.mint .xsi .t/ � ys

i .t///PTtD0 xsent

t

: (3.5)

Once more, an FR will correspond to a negative result (�i < 0), which happenswhen the surplus of received payments relative to the total amount of all paymentssent through the system (denoted by the second term in (3.5)) exceeds its net debitposition relative to the total amount of payments received in the system (the first termin (3.5)).






3.2 Measuring freeriding on liquidity in CUD

The aforementioned liquidity provision measures were calculated using data collatedbetween November 2014 and April 2016 for the number of payments registered persecond during CUD’s operating hours (from 07:00:00 to 19:59:59). We identify twosources of heterogeneity in the data: between and within groups. The set of graphspresented in Figure 2 reveal the first type of heterogeneity in the differences observedper type of financial institution. To address the heterogeneity within groups, we presentthe results for each type of financial institution located in the median of the distribution(the fiftieth percentile).

As can be seen from Figure 2, panels (a)–(e), these daily measures do not share thesame measurement scale. In effect, the cost- and risk-based measures move withinthe same interval, which can be attributed to the fact that they are based on eachinstitution’s net debit position. The same happens with the time-based measure andthe early payment indicator, both of which are adjusted for the number of minuteswithin the system’s operating time (780).

Even so, considering the median value per institution, the estimated measuresproduce negative results, which suggests that some financial institutions (financialcorporations, brokerage firms and trust companies) settled their own payments usingothers’ liquidity. In a strict sense, these results cannot be considered conclusive ofthe FR problem because they indicate, at most, the existence of apparent FRs, but notwhether this conduct arises from individual institutions’ willingness to delay. Hence,a more formal evaluation, as undertaken by Denbee et al (2012, 2015), is required totest for this problem in the Colombian case.

4 METHODOLOGY AND ESTIMATION RESULTS

The estimation methodology that we follow is composed of a three-step procedure.In the first step, we constructed a fictitious month of intraday payments by taking arandom sample of observations from the entire period that runs from November 1,2014 to April 12, 2016. Specifically, we randomly selected a day representing theposition of a day in a month, using a vector of random numbers from dates across theentire period, excluding holidays. This vector of random numbers was constructedwith the setting of a specific seed so that the process might be replicated. In thisprocedure, we also assume a continuous uniform distribution in the open interval.0; 1/, such that there exists the same probability of choosing a certain day within thesame type of day (a Monday from all Mondays, a Tuesday from all Tuesdays, etc).6

6 Denbee et al (2012) construct such a simulated data set using the payments registered in CHAPSin the course of 102 days.






FIGURE 2 Measures of liquidity provision (financial institution in the median of thedistribution).

10/3

1/14

12/3

1/14

02/2

9/16

06/3

0/15

08/3

1/15

02/2

8/15

04/3

0/15

10/3

1/15

12/3

1/15

–0.007–0.005–0.003–0.0010.0010.003

0.0070.005

Banks Brokerage firmsTrust companiesFinancial corporations

(a)

–0.7

–0.5

–0.3

–0.1

0.1

(c)

–0.007–0.005–0.003–0.0010.0010.003

0.0070.005

–0.7

–0.5

–0.3

–0.1

0.1

(d)

–3

–2

–1

0

1

2

3 (e)

(b)

10/3

1/14

12/3

1/14

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9/16

06/3

0/15

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02/2

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1/15

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1/15

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1/14

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1/14

02/2

9/16

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0/15

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1/15

02/2

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0/15

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1/15

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1/15

10/3

1/14

12/3

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0/15

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02/2

8/15

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0/15

10/3

1/15

12/3

1/15

(a) Cost-based measure .�i /. (b) Risk-based measure .�i /. (c) Time-based measure .ıi /. (d) Early paymentindicator .�i /. (e) Relative net sending indicator .�i /. Sources: Banco de la República; authors’ calculations.

Thus, assuming that our constructed month starts on a Wednesday, this month is givenby the payment data corresponding to the order of dates presented in Table 2.

In the second step, we simulated a set of payment data by randomly rearrangingthe payments depending on the institution’s willingness per second within each day,






TABLE 2 Randomly selected days.

Week Wednesday Thursday Friday Monday Tuesday

1 02/18/2015 07/09/2015 08/21/2015 12/15/2014 10/06/20152 01/13/2016 12/04/2014 05/22/2015 03/28/2016 03/15/20163 03/30/2016 04/30/2015 02/19/2016 09/21/2015 02/24/20154 04/06/2016 11/20/2014 06/05/2015 04/27/2015 06/09/2015

Source: authors’ calculations.

under the assumption that a financial institution might possibly have sent out eachpayment in a different second from what was registered in the CUD. The reorderingof payments is conducted with the aim of breaking any dependency that the paymentsvariable may exhibit, but also with the purpose of defining “the amount of liquiditythat every financial institution might use to make payments absent from behavioralbiases” (Denbee et al 2012, p. 68). The assumed random sampling is based on thefact that each financial institution fulfils all of its payment obligations within the sameday and, hence, the reordering of payments depending on individual willingness ispossible. Accordingly, we set all payments that cannot be settled at a different timeof the day (ie, time-critical and automatic payments) as fixed, given that the sender’swillingness to delay payments is irrelevant in such cases and, therefore, they do notreflect whether or not a financial institution will adopt a strategic payment behavior.Payments solved in the netting cycles or sent by financial infrastructures (includingautomatic clearing houses, payment networks, the central counterparty clearing houseand the foreign exchange clearing house) were also kept fixed, as well as those receivedfrom and sent to the Colombian Central Bank (Banco de la República).7 That is, weonly rearrange the payments that can be temporarily delayed within the same day,between the hours of 07:00 and 19:59. In the case of tax payments, this rearrangementwas done between the system’s opening time (07:00) and 11:00, since this is the lasttime at which these payments can be sent to the National Treasury Directorate.8 Asa result, the rearranged payments correspond with approximately 31.8% of the totalnumber of payments settled in the CUD.

In the third step, we computed the measures of freeriding with the simulated data(obtained from random rearrangements of observed data). These measures were esti-mated almost 160 000 times, corresponding to the financial institutions that sent outpayments in the CUD (eighty financial institutions), times twenty working days in our

7 The aforementioned clearing houses are Sistema de Compensación Electrónica Nacional Inter-bancaria (ACH–CENIT) and Automated Clearing House (ACH) Colombia.8 These tax payments include taxes on sales, income tax, patrimony tax, consumptions tax, wealthtax and the tax collected on imports (import duties).






fictitious month, times one hundred repetitions.9 However, our effective sample sizewas made up of 143 255 observations, because not all financial institutions participatedin CUD on all the selected days.

4.1 A model-based approach

Following Denbee et al (2012), we used regression techniques to estimate individualmodels for each of the computed measures of liquidity provision as a function of a setof explanatory variables (individual characteristics) that may have had an influenceon financial institutions’ funding decisions within the Colombian LVPS. We thencomputed the expected freeriding measures . Oyi / using quantile regression models inthe fifth percentile of the empirical distribution. In this way, the expected measuresare considered as thresholds for determining whether or not a financial institution isexhibiting freeriding behavior at a 5% level of significance (that is, the probability ofcommitting a type I error that corresponds to rejecting the null hypothesis when it istrue). Thus, the fifth percentile of the distribution of a freeriding measure, given the setof individual characteristics (y=X), indicates that 5% of the values of the freeridingmeasure computed from the observed data are less than (or equal to) the specifiedfunction of X .10

We estimated individual models for each measure so that the expected level offreeriding could be computed and hence used as a benchmark from which the existenceof this payment strategy could be established. The explanatory variables includedin the empirical estimations are the ratio between payments sent and received, thevalue of payments sent, the opening balance, the turnover ratio, the average size ofpayments sent, and the average size of payments received (see Table A1 in the onlineappendix for summary statistics). Our expectations for the estimation results were asfollows.

� The ratio between payments sent and payments received is computed as aquotient of the total value of outgoing to incoming payments. A ratio higherthan 1 will correspond to a system participant who has sent out paymentsexceeding the number of payments it collects from other system participants.From this definition, we expect to find a positive relationship between this ratioand all freeriding measures.

9 Denbee et al (2012) used 102 days of observed real payments data from twelve banks and randomlyrearranged each day 200 times, hence obtaining 244 800 observations.10 As Koenker and Bassett (1978) noted, the parameter estimates of the relationship between adependent variable and its explanatory variables in quantile regression models can be obtained forall parts of the distribution of the response variable. Hence, a given quantile Q� is the value suchthat �% of the mass of the distribution is less than (or equal to) Q� .






� The size of a financial institution corresponds with its individual share of theoutgoing payments (in value) relative to the total value of payments executed perday. A negative relationship between this variable and the freeriding measuresis foreseen, which, as in Denbee et al (2012), will reflect the fact that largerparticipants may possibly be tempted (in terms of payments) to take advantageof others’ liquidity for the funding of their own payments.

� The opening balance on each account at 07:00 is included as an individual shareof the total amount registered for all system participants so as to identify theparticipants with the largest balances at CUD’s opening time. On the conditionthat the opening balance represents an alternate source of liquidity to incomingpayments (that capture others’ liquidity), we hypothesize a positive relationshipwith the freeriding measures.

� The turnover ratio of liquidity was included with the aim of assessing the extentto which incoming payments are used to execute payments. Hence, a positiverelationship is conjectured between this variable and all computed measuresof freeriding. This ratio is given by the quotient between the total value ofexecuted payments and the opening balance.

� The average sizes of payments sent and received were included separately, inthe form of the daily mean value of payments registered. We do not anticipateany hypothesis regarding these two variables; instead, we look to the modelestimation to reflect any relationships they may have with the measures offreeriding.

As can be seen from Table 3, the goodness-of-fit measure surpasses 50% in threeout of five freeriding indicators: that is, the cost- and risk-based measures and therelative net sending indicator. For the other two freeriding measures, the pseudo-R2

is somewhat low at 9% and 8%. The individual significance is reported in parenthesesand corresponds with robust standard errors, computed with the aim of correctingproblems of heteroscedasticity that were detected with Machado–Santos-Silva tests(Machado and Santos-Silva 2000) in our preliminary estimation results (not shown).We also report the result of the mean variance inflation factor (VIF), which suggeststhat individual problems of multicollinearity are absent from our specifications. Thisresult is consistent with the low values of the unconditional correlation matrix.

For the fictitious month of payments used in the models’ estimation, the total liq-uidity that these financial institutions required to make payments .L D

PNiD1 Ls

i =s/

averaged 6.9 billion Colombian pesos (COL$) per day, while from random walk sim-ulations this value averaged COL$7.6 billion. Therefore, as in Denbee et al (2012),






in aggregate terms, the liquidity usage computed from simulations results in approxi-mately the same observed liquidity usage, which indicates the absence of any substan-tial difference in the system’s efficiency arising from strategic delays and randomizedpayments.11

Our estimation results for the ratio of payments sent and received are significantlyand positively related in the case of three out of five freeriding measures, based oncosts, risks and the relative net sending indicator. These results suggest that, the moreliquidity provided by participants to the system, the more freeriding measures willincrease (signaling less freeriding). Given that this variable represents the simplestway to determine whether or not a system participant is under providing liquidityto the system, this positive relationship with the freeriding measures is a reasonableoutcome.

As in Denbee et al (2012), the institution’s size (defined in terms of payments) neg-atively affects the cost- and risk-based measures of freeriding, indicating that financialinstitutions with higher payments obligations may cause the extent of freeriding onliquidity to increase. Indeed, financial institutions with larger payments obligationsare those that have higher balances because they are obliged to maintain reserverequirements (this applies to banks and other depositary financial institutions such asfinancial corporations). However, the effect that this variable exerts on the time-basedmeasure and the early payment indicator is positive, signifying an effect in the oppo-site direction. These contrasting results underline a substantial difference betweenthe freeriding measures used; whereas the measures based on cost and risk, and theearly payment indicator may be considered as “full-day” indicators of freeriding, thetime-adjusted indicators may be considered as “mid-day” indicators because they arerelated to a specific part of the day and to a specific portion of the payments (ie, theaverage of payments). Consequently, when comparing these measures it is possibleto find financial institutions that might be labeled as FRs at a certain moment of theday, without being regarded as such when full-day measures are considered. Hence,under the assumption that this variable is indicative of the individual sizes of marketparticipants, the positive relationships found with the time-based measures suggestthat financial institutions with large-sized payments may prefer to transfer half oftheir payments using their own funds. When it comes to understanding their full-dayfunding of payments, however, it is very likely that they were more willing to freeride on others’ liquidity, using these funds to provide for their huge liquidity needs.

As regards the opening balance, the positive sign of the estimated regression coef-ficients suggests that, the more willing financial institutions are to send payments

11 We compute a simulation of random walk using the characteristics of bilateral institutions andassuming an exponential distribution of payments, with a mean given by the average value ofpayments .vij =wij /.






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financed with their own funds, the more the magnitude of freeriding will be reduced.In other words, the higher the opening balance, the less likely we are to see the underprovision of liquidity to the payment system. Nevertheless, this inference only appliesto system participants that have sufficient balance amounts in the first instance, allow-ing them to execute payments early. Financial institutions that participate in the CUDexhibit considerable differences, among them the opening balance. Indeed, banks typ-ically have have the highest opening balances, while brokerage firms usually have thelowest balance levels at the beginning of the day (zero in almost all cases). Likewise,a positive relationship was found with the turnover ratio of liquidity, signifying (asexpected) that freeriding on liquidity will be reduced to a greater extent when morefinancial institutions opt to back their payments with their own funds. This result isintuitive, in the sense that a high turnover ratio signals high liquidity use pertaining(perhaps) to a financial institution that is over providing liquidity to the system.

The average sizes of payments sent and received enter in models’ estimation withsimilar coefficients but extremely low values, and with opposite signs. Consequently,although significant, these variables are considered to be of low explanatory powerand of little use in understanding the provision of liquidity at individual level.

The under provision of liquidity that may arise from this strategy of paymentcan be related to the individual characteristics of each system participant; however,determining whether this may become a problem for the entire payment system maydepend on the number of financial institutions behaving this way. Hence, the overallextent of freeriding is inferred by comparing the observed measures .yi / with themodels’ results . Oyi / and counting the number of instances in which each freeridingmeasure obtained from payments simulations exceeds the measures computed fromthe models’ estimation.

According to our models’ results, financial institutions exceeding the thresholdfor determining freeriding behavior – and, therefore, existence of FRs in the CUD– are indicated by the time-based measure (6.7%) and the early payment indicator(6.7%), since both exceed the 5% level that should have been found for the computedquantile. However, this is not the case when considering the results obtained for thecost-based measure (4.9%), the risk-based measure (5.0%) or the relative net sendingindicator (5.0%). Hence, according to the results obtained from mid-day measures andrelated to a certain percentage of payments (50% of payments under the time-basedmeasure and the early payment indicator), certain financial institutions may be labeledFRs on liquidity, without necessarily being regarded that way according to full-daymeasures. Similar results were obtained when we estimated the same specificationused by Denbee et al (2012) (see Table A2 in the online appendix). These results alsoindicate that financial corporations are the only group that free rides in the system, ascan be noted from the measures based on cost (16.9%) and risk (17.5%).






TABLE 4 Values obtained from models’ results with a 5% threshold (all values shown arepercentages).

Cost- Risk- Time- Early Relative netbased based based payment sending

measure measure measure indicator indicator�S

i�S

iıi �i #i

Threshold value 4.9 5.0 6.7 6.7 5.0exceeded acrossall institutions

Banks 4.8 4.9 4.1 3.8 2.3

Financial 16.9 17.5 4.3 3.5 3.1corporations

Trust 1.4 1.4 5.1 6.6 7.7companies

Brokerage 4.3 4.3 4.3 3.0 3.1firms

Institutions that 57.5 57.5 18.8 18.8 1.3never exceededthe threshold

Another interesting result from the full-day measures (based on cost and risk cri-teria) is that more than half of system participants (57.5%) never under providedliquidity to the system, which may explain why (under normal circumstances or onnormal days), despite the existence of some FRs, their negative effects on the CUDare nil.

4.2 A model-free approach

The extent of liquidity provision can also be assessed using the empirical quan-tiles alone. As in Denbee et al (2015), these results indicate the number of timeseach freeriding measure fell below the fifth percentile or rose above the ninety-fifth percentile. Accordingly, results below 5% reflect the financial institutions thatunder provide liquidity (as a result of their freeriding behavior), while those above95% correspond to financial institutions that over provide liquidity to the system.

At the aggregate level, the computed quantiles for the 5% threshold indicate thatthe degree of freeriding is normal under the criteria of the measures based on cost(4.2%) and risk (3.6%) as well as the relative net sending indicator (0.5%); how-ever, it becomes worrying when considering the time-based measure (76.4%) and theearly payment indicator (25.7%). In general terms, the inferences that can be drawnfrom these results coincide with those obtained from the model-based approach; some






TABLE 5 Threshold values obtained from empirical quantiles at 5.0% and 95.0% (allvalues shown are percentages).

Early RelativeCost-based Risk-based Time-based payment net sending

measure measure measure indicator indicator�S

i�S

iıi �i #i‚ …„ ƒ ‚ …„ ƒ ‚ …„ ƒ ‚ …„ ƒ ‚ …„ ƒ

Threshold 5.0 95.0 5.0 95.0 5.0 95.0 5.0 95.0 5.0 95.0

Threshold 4.2 19.6 3.6 21.8 76.4 9.2 25.7 26.4 0.5 14.5valueexceededacross allinstitutions

Banks 4.3 41.5 4.1 41.3 64.6 10.0 18.2 31.3 0.2 36.8

Financial 15.5 0.0 11.7 1.4 89.3 6.7 24.0 12.2 0.2 2.8corporations

Trust 1.1 0.0 0.9 13.5 86.4 6.9 33.4 20.0 0.7 0.6companies

Brokerage 5.0 13.2 4.3 16.5 72.1 12.0 24.1 32.9 0.5 11.0firms

financial institutions prefer to rely on others’ liquidity to fund a certain level of pay-ment at a certain moment of the day, but in general terms they behave in a similarmanner to their peers (sharing liquidity) according to full-day measures.

At the individual level, the results vary per type of financial institution. Banks andbrokerage firms behave much like those observed at the aggregate level, with someof them acting as FRs according to mid-day freeriding measures (ie, time based andearly payment) but not under full-day measures (ie, cost and risk based). In the caseof brokerage firms, these results can be explained, to some extent, by the fact that theyusually have no liquidity at the beginning of the day. Therefore, it is very likely thatthe under provision of liquidity identified from mid-day measures is unintentionalfor these financial institutions; rather, it partially reflects the way brokerage firmsmanage their liquidity (according to what is common for their business type). Incontrast, according to four out of five criteria the group represented by financialcorporations is labeled FR. Although this result can be related to their obligation tomaintain reserve requirements, the identified FR behavior represents only the largesttwo financial corporations.

Provided the time of an incoming payment coincides with the time at which thepayment should be executed, the recycling of payments need not be seen as worrying.However, when there is a considerable mismatch between these two payment flows,






reusing others’ liquidity may signify several payment delays and become a seriousproblem for the entire payments system. Hence, keeping a close watch on thesemeasures can be a useful way for us to gauge which system participants are fundingtheir payments as well as the moment at which the extent of liquidity hoarding deservesattention.

At the other extreme, the empirical quantiles at 95% indicate the financial institu-tions that are over providing liquidity to the system. As can be seen from the obtainedresults, this group is mainly composed of banks, followed by some brokerage firmsand trust companies.

Summing up, we found evidence of the existence of FRs in CUD, mainly supportedby the relative measures associated with a certain number of payments and a certainmoment of the day (mid-day measures). However, when considering results per typeof system participant, the empirical quantiles point to financial corporations as thegroup that hoarded liquidity most of all. The results obtained for the period of study arenot worrisome; however, a call for close oversight of these measures on an individualbasis and more recent data may be of interest, not only for system managers but alsofor the purposes of oversight in general.

5 CONCLUSIONS

Financial institutions’ intraday payments can be funded using loans (from the centralbank or the money market), their own deposits at the central bank and/or the paymentsthey receive from other system participants. Of all these liquidity sources, only thelast one is available free of charge and, hence, it is usually preferred above the others.

The strategy of funding payments using only the disbursements received fromcounterparties is known as the FR strategy. Although it is considered optimal from anindividual perspective, this is not the case for the entire payment system, because itmay imply payment delays arising from the mismatch between the moment in whicha financial institution has to send a payment and the moment in which it receivesa payment. In LVPSs operating in RTGS mode, the existence of several financialinstitutions behaving as FRs may lead to the under provision of liquidity, impeding,as a result, the completion of a considerable amount of payments in a timely manner.

Our results for the financial institutions participating in CUD evidence the existenceof FRs on liquidity, most commonly found in financial corporations. However, thereis also evidence of this payment strategy within other groups. Since this problem mayeven generate difficulties for system participants that are expecting to collect paymentson time, a call for action on this matter should be considered so as to discourage theadoption of this strategy. Possible solutions to the FR problem might entail the designof a scheme based on penalties (fees), incentives (discounts) or deterrence measures(such as the throughput rules adopted in the UK LVPS).






Some interesting extensions of this subject could include an empirical assessmentof what drives freeriding behavior, considering the extent to which the existence ofFRs can be related to a rise in the volatility of liquidity, the monetary stance of thecentral bank and/or risk aversion, among many other potential causes.


The views expressed in this paper are the sole responsibility of the authors and do notreflect those of Banco de la República or its board of directors. The authors report noconflicts of interest. The authors alone are responsible for the content and writing ofthe paper.

ACKNOWLEDGEMENTS

We want to thank Carlos Cadena for his collaboration with data handling. We are alsothankful for the comments and suggestions we received from Pamela Cardozo, ClaraMachado, Carlos León and the staff of the Monetary and International InvestmentDivision. Special thanks to Rodney Garratt, Peter Zimmerman and participants in theEconomics of PaymentsVIII Conference (hosted by the Bundesbank, 9–10 November,2016).

REFERENCES

Angelini, P. (1998). An analysis of competitive externalities in gross settlement sys-tems. Journal of Banking and Finance 22(1), 1–18 (https://doi.org/10.1016/S0378-4266(97)00043-5).

Banco de la República (2016). Payment system report. Report, June, Banco de laRepública.

Becher, C., Galbiati, M., and Tudela, M. (2008). The timing and funding of CHAPS sterlingpayments. Federal Reserve Bank of New York: Economic Policy Review 14(2), 113–133(https://doi.org/10.2139/ssrn.1141340).

Committee on Payment and Settlement Systems–Technical Committee of the Interna-tional Organization of Securities Commissions (2012). Principles for financial mar-ket infrastructures. Report, April, Bank for International Settlements. URL: http://bit.ly/1mcqA8x.

Denbee, E., Garrat, R., and Zimmerman, P. (2012). Methods for evaluating liquidity pro-vision in real-time gross settlement payment systems. In Diagnostics for the FinancialMarkets – Computational Studies on Payment System: Simulator Seminar Proceedings,2009–2011, Hellqvist, M., and Laine,T. (eds), pp.53–76.Scientific Monographs, Helsinki.

Denbee, E., Garrat, R., and Zimmerman, P. (2015). Identification of over and underprovision of liquidity in real-time payment systems. The Journal of Financial MarketInfrastructures 4(2), 1–20






Diehl, M. (2013). Measuring freeriding in large-value payments systems: the case ofTARGET2. The Journal of Financial Market Infrastructures 1(3), 31–53.

Galbiati, M., and Soramäki, K. (2010). Liquidity-savings mechanism and bank behavior.Working Paper 400, Bank of England.

Kahn, C., and Roberds, W. (2009).Why pay? An introduction to payments economics. Jour-nal of Financial Intermediation 18(1), 1–23 (https://doi.org/10.1016/j.jfi.2008.09.001).

Koenker, R., and Baset, G. (1978). Regression quantiles. Econometrica 46(1), 33–50.León, C., Machado, C., Cepeda, F., and Sarmiento, M. (2011). Too-connected-to-fail insti-

tutions and payments system’s stability: assessing challenges for financial authorities.Borradores de Economía 644, Banco de la República.

Machado, J. A. F., and Santos-Silva, J. M. C. (2000). Glejser’s test revisited. Journal ofEconometrics 97, 189–202.

Martínez, C., and Cepeda, F. (2015). Reaction functions of the participants in Colombia’slarge-value payment system.The Journal of Financial Market Infrastructures 4(2), 21–47(https://doi.org/10.21314/JFMI.2015.051).

McAndrews, J., and Potter, S. (2002). Liquidity effects of the events of September 11, 2001.Federal Reserve Bank of New York: Economic Policy Review 8(2), 59–79.

Nellen, T. (2011). Essays in payment economics. Report, Swiss National Bank.






Research Paper

Measuring system-wide resilience of centralcounterparties

Stathis Tompaidis

Information, Risk, and Operations Management Department, McCombs School of Business,University of Texas at Austin, 2110 Speedway Stop B6500, Austin, TX 78712-1277, USA;email: [email protected]

Office of Financial Research, US Department of the Treasury, 717 14th Street Northwest,Washington, DC 20220, USA

(Received August 16, 2017; accepted April 10, 2018)

ABSTRACT

Central clearing has the potential to improve transparency and risk management acrossthe financial system. It also creates new dependencies between firms. Stress tests arenow a common tool used by central counterparties (CCPs) to evaluate their resiliencein the face of losses caused by the default of their clearing members. Determining theresiliency of the entire system of CCPs and clearing members, however, requires acombined supervisory stress test across both CCPs and their clearing members. Wepropose a framework for extending existing stress test results obtained at individualCCPs to create a system-wide stress test.

Keywords: central clearing; central counterparty (CCP) supervision; supervisory stress tests; Groupof Twenty (G20) Pittsburgh agenda.

1 INTRODUCTION

Following the 2007–9 financial crisis, several regulatory initiatives were introducedduring a Group of Twenty (G20) meeting in Pittsburgh to avoid a repeat of the large

Print ISSN 2049-5404 j Online ISSN 2049-5412© 2018 Infopro Digital Risk (IP) Limited







42 S. Tompaidis

losses sustained in over-the-counter (OTC) derivatives transactions. These initiatives,known as the G20 Pittsburgh agenda, addressed several vulnerabilities of the financialsystem. In the OTC market, the initiatives aimed to improve transparency, mitigate sys-temic risk and protect against market abuse. To achieve these aims, all standardizedderivatives contracts were required to be traded, where appropriate, on exchangesor electronic trading platforms, cleared through central counterparties (CCPs) andreported to trade repositories. In addition, it was decided that noncentrally cleared con-tracts should be subject to higher capital requirements: an incentive for counterpartiesto favor cleared transactions.

In the process of central clearing, a CCP assumes the credit risk associated withthe default of one of the two parties in a financial transaction. Some OTC derivativestransactions were centrally cleared before 2008, but many were not (for a compre-hensive history of central clearing, see Norman (2011)). While central clearing mayincrease transparency and improve risk management, it also concentrates risk. TheCCP becomes a single point of failure that can facilitate contagion. Losses from thedefault of a large clearing member at a CCP may spread to other clearing members.Losses can also spread across markets if the defaulting clearing member belongs tomultiple CCPs for different asset classes, or if losses at one CCP lead to the defaultof clearing members participating in other markets.

Central clearing has been growing rapidly in OTC derivatives markets. CCPs arerequired to maintain adequate amounts of liquid resources to protect themselvesagainst the default of a clearing member. As a result, losses that are large enoughto create ripple effects across the financial system are assumed to be rare. Now is thetime to monitor the system-wide effects of these rare events. Stress testing againstclearing member default, undertaken by individual CCPs and their supervisors to testtheir own resilience under stress, can also be used to monitor the resilience of thesystem as a whole.1

This paper describes the three components needed to simultaneously stress testclearing members and CCPs across markets: scenario generation, evaluation of theprofit and loss (P&L) of clearing member portfolios for each scenario, and default ofclearing members. For each component, the paper provides alternatives and discussestheir benefits and limitations. Among the alternatives, we recommend using factors togenerate scenarios, because this allows for flexibility and for the more straightforward

1 Stress testing may be designed to assess anything from the strength of materials (useful for design-ing buildings resistant to earthquakes or evaluating the performance of firearms, for instance) to ourlevel of cardiovascular health during exercise. Stress testing for financial institutions goes back atleast as far as 1992, when the US Office of Federal Housing Enterprise Oversight was charged withdeveloping a stress test for Fannie Mae and Freddie Mac. Regulatory stress testing expanded afterthe financial crisis of 2007–9 to encompass banks, insurance companies, hedge funds and CCPs.





Measuring system-wide resilience of CCPs 43

generation of many scenarios. We recommend using existing stress test results atindividual CCPs to calculate P&L, because reusing existing information does not placean additional burden on CCPs and clearing members. We recommend using defaultmodels that are consistent with stress scenarios, because consistency across stressscenarios implies that the same standards on the extremity of the market conditionscan be applied to defaults.

All the information needed to carry out a system-wide stress test based on ourrecommendations is already available to supervisors of individual CCPs. Carryingout a system-wide test would require cooperation among these supervisors.

2 THE CURRENT STRESS TESTING LANDSCAPE

The Principles for Financial Market Infrastructures (PFMIs) provide guidelines forstress tests for individual CCPs. These PFMIs were developed in 2012 by the Commit-tee on Payment and Settlement Systems and the International Organization of Secu-rities Commissions (CPSS–IOSCO 2012).2 Figure 1 describes these guidelines. In2016, both the European Securities Market Authority (ESMA) and the US Commod-ity Futures Trading Commission (CFTC) reported the results of the first system-widesupervisory stress tests. Table 1 provides information on these supervisory stress tests.Both the ESMA and CFTC stress tests were designed to satisfy the PFMI require-ments. Later guidelines, developed by CPMI–IOSCO (2017), required a potentiallyseparate liquidity analysis stress test.

2.1 Individual CCPs

Individual CCPs carry out and report stress tests daily. The goal of these tests isto ensure that CCPs are able to continue operating in times of market stress. Stressscenarios include peak historic price volatilities, shifts in market factors that determinethe price of contracts cleared by the CCP, and forward-looking stress scenarios in avariety of extreme but plausible market conditions.

The PFMIs (CPSS–IOSCO 2012) divide all CCPs in two groups. The first groupincludes CCPs that are involved in activities with a complex risk profile and CCPs thatare systemically important in multiple jurisdictions. All other CCPs are placed in thesecond group. Stress tests are used to determine the resources required by the CCPs.CCPs in the first group are subject to Cover 2, that is, their stress tests include thedefault of at least the two participants and their affiliates with the largest exposures.CCPs in the second group are subject to Cover 1 instead. A CCP should have adequatefinancial resources to cover the losses in these stress tests using initial margin andother prefunded financial resources. These additional resources include contributions

2 CPSS is now the Committee on Payment and Market Infrastructures (CPMI).





44 S. Tompaidis

FIGURE 1 PFMIs: CCP stress testing guidelines.

Scenario requirements

• Peak historic price volatilities

• Shifts in market factors

• Forward-looking in a variety of extremebut plausible market conditions

• Reverse stress tests to identify scenarios that would cause the largest exposures

Default requirements

• Systemically important CCPs must withstand default by the largest twoparticipants and their affiliates, by exposure (Cover 2)

• Other CCPs must withstand default by the largest participant and its affiliates,by exposure (Cover 1)

Liquidity requirements

• A CCP must have sufficient liquidresources to withstand losses due to the default of its largest participant and affiliates, by exposure

The PFMIs apply to systemically important entities in the clearing, settlement and recording of financial transac-tions. These include CCPs, trade repositories, swap data repositories, central securities depositories and securitiessettlement systems. Source: CPSS–IOSCO.

by the CCP itself (known as “skin in the game”) and contributions of the clearingmembers to a mutualized fund (the guarantee fund).

In addition to requiring sufficient resources, the PFMIs (CPSS–IOSCO 2012)require sufficient liquid resources. Stress scenarios for liquidity risk include the defaultof at least the largest participant and its affiliates.

2.2 ESMA and CFTC supervisory stress tests

In April 2016, ESMA reported the results of its first stress test of CCPs domiciled inthe European Union (European Securities and Markets Authority 2016). ESMA con-sidered factors that influence the value of contracts cleared at each CCP to constructfour extreme but plausible scenarios. ESMA relied on CCPs to calculate and reportthe P&L for each clearing member.






Given the P&L of each clearing member, ESMA considered the defaults of anincreasing number of clearing members. Losses were resolved according to the defaultwaterfall for each CCP. In its report, ESMA provided estimates of the shortfall acrossthe entire system. For example, ESMA found that the default of the two clearingmembers with the largest losses at each CCP would exhaust the CCPs’ prefundedresources and result in an additional, system-wide shortfall of €10 billion.

ESMA’s stress test (2016) has both advantages and limitations. One advantage isthat, since it uses factors already considered by CCPs for their individual stress tests, itplaces a relatively modest additional burden on CCPs. The authority’s report mentionslimitations including the small number of days over which the losses are calculatedand the focus on credit risk to the exclusion of liquidity, operational, investing, fundingand other kinds of risk.

In November 2016, the US CFTC reported the results of a joint stress test ofthe five largest clearing houses registered with them: Chicago Mercantile Exchange(CME) Clearing, Intercontinental Exchange (ICE) Clear Credit, ICE Clear Europe,ICE Clear US and London Clearing House (LCH) Clearnet Limited (CommodityFutures Trading Commission 2016). Focusing on the largest clearing firms, CFTCdeveloped a set of eleven stressful scenarios based on price changes and correlationsacross markets that occurred on dates with extreme volatility. The results show thatCCPs have sufficient prefunded resources to cover losses to the two largest clearingmembers at each CCP. The results also show that clearing members are diversifiedacross CCPs.

The CFTC stress test (2016) shares the limitations of the ESMA stress test (2016)but improves on it by considering a larger number of scenarios. Despite these lim-itations, the ESMA and CFTC supervisory stress tests represent a clear advance inassessing the resiliency of both CCPs and clearing members.

Both ESMA and CFTC updated their stress tests in 2017. Both stress tests wereimproved. ESMA changed its method of generating scenarios to ensure that they areconsistent across CCPs and also introduced a liquidity stress test; CFTC considereda methodology that chooses scenarios iteratively but did not implement a liquiditystress test.

3 SYSTEM-WIDE SUPERVISORY STRESS TESTS FOR CENTRALCOUNTERPARTIES AND CLEARING MEMBERS

The literature on supervisory stress testing has largely focused on the banking andinsurance sectors (for studies in the European context, see Drehmann (2009), Henryand Kok (2013) and Dees et al (2017)). These stress tests have been critically evaluatedbyAlfaro and Drehmann (2009) and Borio et al (2012). The supervisory stress tests forfinancial institutions and CCPs show various similarities and differences. As regards





46 S. Tompaidis

TABLE 1 2016 US and European supervisory stress tests.

US CFTC ESMA

Who Five CCPs supervised by Seventeen CCPs supervised by ESMAtested? CFTC All clearing members at each CPP

Fifteen clearing membersper CCP

When One date: April 29, 2016 October 31, November 30 andtested? Results released December 31, 2014

November 16, 2016 Results released April 29, 2016

General Eleven scenarios: all Four scenarios: three hypothetical;scenarios hypothetical across CCPs one historical eventtested? Respect observed correlations Represent extreme but plausible

conditions at each CCP

Default Considers the effect of Considers the default of a CPP’sscenarios successive defaults of � two largest clearing memberstested one, two and eventually by exposure,

all clearing members � two largest corporate groupsby exposure,

� two largest corporate groupsby default-probability-weighted exposure

Prefunded resources used Prefunded and unfunded resourcesto determine a CPP’s used to determine CCP’s financialfinancial shortfall shortfall

Reverse stress test considers how manydefaults required to exhaust prefundedand total financial resources

Test Small number of scenarioslimits Considers only credit risk, not other risks such

as liquidity, operational, investment, funding

Sources: CFTC; ESMA.

similarities, both try to estimate the resilience of the system simultaneously: in the caseof financial institutions, the resilience of multiple banks and the financial entities thatare exposed to them; in the case of CCPs, multiple CCPs and their clearing members.In terms of differences, supervisory stress tests for banks involve macroeconomicscenarios, such as the impact on profitability or deposits of a downturn in economicactivity. For CCPs, however, scenarios are mostly focused on shocks to prices, andpotentially the liquidity, of the financial instruments cleared by the CCP.






Our supervisory stress test for CCPs may be broken down into the following threecomponents:

� a set of scenarios that reflect the sensitivities of all the entities being tested;

� the estimation of the P&L for each entity under each scenario; and

� the determination of clearing member defaults.

We discuss the benefits and limitations of the various alternatives for each com-ponent. Based on the trade-offs between the benefits and limitations, we provide arecommended approach for each component.

3.1 Scenarios

The goal in choosing scenarios is to identify extreme but plausible conditions, mea-sured by the size of the potential losses. The literature focuses on choosing scenariosjudiciously, that is, using only a small number of pertinent scenarios. One reasonto limit the number of scenarios is the difficulty in calculating the P&L profiles oflarge portfolios in each scenario (see Pritsker (2012), Glasserman et al (2015) andthe references in Bookstaber et al (2013)).

In the case of a system-wide stress test, identifying the appropriate scenariospresents a challenge. It is difficult to know in advance which scenarios will be the moststressful for the entire system. As a result, many scenarios may need to be considered.Fortunately, in the case of CCPs, this is not a significant concern. Each CCP carriesout stress tests where the P&L for the portfolio of each clearing member is calculatedfor tens, hundreds or even thousands of scenarios.

One way to combine these scenarios across CCPs and clearing members is tocalculate the P&L of every cleared contract using changes over particular calendardates. A benefit of this approach is that knowing the P&L of every cleared contractgiven the change over a particular calendar date allows each CCP to calculate theP&L of clearing member portfolios for changes over the same calendar dates. Theresults obtained may then be combined across various CCPs. For example, changesover each calendar day during the previous five years can be used. A limitation of thisapproach is that historical changes may not include conditions that are extreme butplausible.

The ESMA and CFTC stress tests do not rely solely on historical scenarios. Toensure that extreme but plausible conditions are included, they construct scenariosbased on historical information, information implied by the prices of traded instru-ments, and expert judgment. One limitation of their stress tests is that the tests onlyconsider a small number of scenarios, chosen to represent extreme but plausible con-ditions for cleared contracts. To determine whether extreme but plausible conditions





48 S. Tompaidis

for contracts also translate to extreme but plausible conditions for clearing memberportfolios, we may need to consider many scenarios.

One easy way to construct many scenarios is to use a common set of factors.Common factors might include interest rates, prices of equity and commodity indexes,market volatility or aggregate default rates. We can use historical information as wellas information on market prices and expert judgment to determine a distribution thatcaptures how the values of the factors change. Scenarios are generated in a way thatis consistent with the joint distribution of factors.

Factor models make parametric assumptions regarding the joint distribution ofthe factors, rather than simply relying on realized changes. For example, factors areassumed to follow correlated normal or lognormal distributions, or copula models. Theparameters of the models are determined using historical information. Such modelsmake generating many scenarios straightforward. Once scenarios have been gener-ated, we can identify the scenarios that result in the greatest system-wide stress andrefine the assumptions underlying the distribution of the factors.

Factor models typically involve only a few variables. To calculate the P&L ofindividual contracts, we also need a model that links the common factors with thecontract prices. For example, consider a CCP that clears credit default swap (CDS)contracts for both indexes and individual companies.3 Changes in the price of CDScontracts are given by changes in the spread between the risk-free rate and the rate onthe corresponding bonds. We can decompose these changes for all contracts into onecommon factor – the spread on the index – and one residual term for each contract.Given the distribution of the common factor, ie, the spread on the index, and thebreakdown of individual companies into spreads, we can generate any number ofscenarios. Similar factors can be used for other asset classes, such as interest rates ofdifferent terms and denominations for bonds and interest rate swaps, exchange ratesfor exchange rate derivatives, and the prices of commodities for commodity futuresand commodity derivatives.

We recommend using factor models for two reasons. They are flexible and can beused to model a large number of changes with a few common factors, and they caneasily be used to generate a large number of scenarios.

3.2 P&L

The second component of a stress testing framework is the calculation of P&L profilesfor the clearing member portfolios. To aggregate P&L across CCPs and clearing

3 A CDS contract on an individual company provides protection against losses, incurred by thedefault of the company, to the value of the company’s bonds. A CDS contract on an index providesprotection against losses to the value of a basket of bonds of several companies.






FIGURE 2 Supervisory stress testing: existing and proposed components.

Existing Proposed

Historical data+ market information

+ expert judgment

Increase number ofscenarios, usefactor models

Cover 1Cover 2Cover all

Use structural model

Based onactual positions

Based on stresstests + interpolation/

approximateportfolios

P&L of eachclearing memberin each scenario

Stress testscenarios

Default ofclearing

members

Source: author’s analysis.

members, we must first calculate P&L for the portfolio of each clearing member ineach scenario.

To do this, the existing ESMA and CFTC tests use clearing member positions.Knowing the portfolio positions in individual contracts, we can calculate the P&Lof the portfolio by first calculating the P&L values of the individual contracts.4 Intheir supervisory stress tests, both ESMA and CFTC used information on positionsto calculate portfolio P&L. ESMA relied on CCPs to perform this calculation. CFTCperformed its own calculation and verified the results with the CCPs. The calculationrequires information on the positions of the clearing members and can be complicatedfor certain cleared contracts.

We recommend something different: using stress test results reported by individ-ual CCPs. The advantage of using stress tests is that they do not require positioninformation, and they do not place any additional burden on the CCPs or clearingmembers. The portfolio P&L can be approximated either directly by using numericalinterpolation or indirectly by constructing portfolios of instruments cleared at eachCCP that approximate the portfolio of each clearing member.

4 Campbell and Ivanov (2016) propose using the actual portfolios of clearing members to evaluateP&L across CCPs using models built by supervisors.





50 S. Tompaidis

Interpolation relies on the idea that if, for example, we know the change in the valueof a portfolio when interest rates move by 2, 5 or 10 basis points (bps), then we canapproximate the change in value for intermediate changes. For interpolation to workwell (that is, for the interpolation error to be small), many scenarios at individual CCPsare needed. The scenarios should accurately capture portfolio value changes whenmultiple variables change simultaneously. For example, in the case of a portfolio thatdepends on interest rates and implied volatilities, it is not enough to know how theportfolio value changes when interest rates or implied volatilities vary independently.We also need to know how the portfolio value changes when interest rates and impliedvolatilities change simultaneously.

There is a way to address this problem: with the construction of approximate port-folios. To illustrate how CCP stress tests can help construct an approximate portfolio,consider the case of a CCP that clears a single contract, for example, an index. Let ussuppose that a 20% increase in the value of the index results in a loss of US$2 mil-lion, implying that the portfolio is short $2 million=20% D $10 million in the index.When a CCP clears multiple contracts, more scenarios are needed, but the idea is thesame. In that case, rather than solving for a single unknown in a single equation, onesolves for many unknowns in a system of equations. We note that it is not necessaryto build portfolios that reflect the entire range of contracts cleared by the CCP. Usingfewer contracts may result in a less accurate approximation, but it has the benefit ofneeding fewer scenarios.

The possibility of constructing approximate portfolios solves the problem ofcalculating P&L for a portfolio from stress test information when variables arechanged independently. In that case, the P&L for the portfolio is calculated usingthe approximate portfolio.

The accuracy of either the interpolation or the approximate portfolio approachdepends on the number of stress test scenarios available at the CCP: the more, thebetter. Having many scenarios available also allows for an evaluation of the accu-racy of either approach. Borrowing ideas from machine learning, one can evaluateaccuracy by using some of the CCP stress tests (the training set) to build the approxi-mation, and then using the remaining stress tests (the validation set) to test whether theapproximation matches the reported results (see Hastie et al 2009). Alternatively, wecan determine accuracy over time by backtesting the projected changes in portfoliovalue against the actual changes observed.

3.3 Default

The last component of a system-wide supervisory stress test for CCPs and theirclearing members is the determination of clearing member default in each scenario.Determining default is important because CCPs hold balanced portfolios. If clearing






members do not default, CCPs face no losses, no matter how large the movements incontract prices.

The existing approaches employed by ESMA and CFTC do not use a model todetermine whether default is probable. Rather, they determine default on the basisof the size of the relevant exposures. Cover 1 and Cover 2 are standards used forindividual CCPs. ESMA and CFTC consider variations for supervisory stress tests.For example, ESMA considers scenarios where the two biggest clearing members inevery CCP default, while CFTC considers scenarios where the biggest, two biggestor all the clearing members default.

While a model-free approach is attractive, there is a drawback: it is difficult todetermine if defaults are extreme but plausible. Beyond facing CCPs, clearing mem-bers have bilateral exposures toward other financial/nonfinancial entities. They haveassets and liabilities whose values fluctuate. In many cases, this means that a clearingmember may face large losses in its cleared portfolio but not default. Given that clear-ing members are similar in many ways, it is likely that, in some scenarios, multipledefaults will occur simultaneously. To determine whether a particular combination ofdefaults is extreme but plausible, we need a default model that is consistent with thestress scenarios used.

If assets, liabilities and all bilateral exposures in each asset class between clearingmembers are available, they can be used to determine whether or not a clearingmember will default in a particular scenario. However, it is unlikely that such acomplete picture of the financial system will be available. There is an alternative wayto determine simultaneous defaults based on structural models of firm value.

In a structural model, default is determined based on information on firm liabilitiesand changes in firm value.5 Similar to our discussion on factor analysis for buildingscenarios, changes in firm value may be divided into numerous factors. For example,in the case of a large bank, a structural model could determine how equity pricesdepend on interest rates, index CDS spreads, mortgage default rates and the Standard& Poor’s 500 (S&P 500). Given a stress scenario where these factors change, we candetermine whether the firm defaults by comparing the resulting firm value with theface value of the liabilities. Determining defaults this way is compatible with factoranalysis for scenario building; it is possible, for example, to determine default usingfactors other than those used to generate scenarios for valuing cleared contracts. Insuch a case, we would augment the information in the scenarios to include both setsof factors.

We recommend using a structural model to determine multiple defaults, not becauseit is any more or less strict than the model-free approach, but rather because it isconsistent with the stress scenarios. It is difficult to determine whether defaulting the

5 The value of a firm is the sum of the value of its liabilities and the value of its equity.





52 S. Tompaidis

two largest members by exposure in every CCP is extreme but plausible in a givenscenario. With a structural model, defaults are consistent in each scenario. In somecases, this means that a greater number of firms will default. In other cases, there willbe fewer. Nonetheless, defaults would be consistent for each stress scenario.

3.4 Reverse stress testing

Reverse stress testing attempts to identify the conditions that lead to the largest system-wide losses. Losses depend on both clearing member defaults and price changes. Ifmany scenarios are available, the scenarios with the largest losses may be a reasonableapproximation. An alternative approach would entail searching for the conditions thatlead to the largest losses iteratively, using information from existing scenarios. Onecaveat with regard to this is that searching for the worst scenarios iteratively may alertCCPs to the concerns of supervisors. This provides an additional reason to calculatethe P&L of the portfolios of clearing members using stress test results, rather thanrelying on CCPs to calculate or verify P&L estimates using clearing member positions.

3.5 Extensions: accounting for reduced liquidity and auction failure

Adjustments for market liquidity are necessary when a clearing member holds a verylarge portfolio. To illustrate this problem, consider a situation where a clearing mem-ber defaults while holding 30% of the outstanding positions in a particular market.The size of such a portfolio may be many times the average daily, or even weekly,market trading volume. While CCPs use auctions to avoid liquidating large positionsof defaulting members in the open market at times of stress, transferring a positionof this size is likely to have an additional price impact beyond what is reflected inhistorical experience. Liquidity adjustments need to consider the size of the portfolioof the defaulting member relative to the market volume and to the exposures of thesurviving clearing members.

Liquidity adjustments also need to consider whether the transfer auction followingdefault may fail. If winning the auction results, for example, in the violation of one ofthe requirements that the clearing members must fulfill, clearing members may notparticipate in the auction. If the auction fails, the position may need to be liquidatedin a stressed market place, and a larger adjustment may be needed.

4 CONCLUSION

We have described a framework for carrying out a system-wide stress test of CCPsand clearing members. We recommend three changes to the current supervisory tests:the use of factor analysis to generate a large number of scenarios, the use of stress testresults to calculate P&L for clearing member portfolios in each scenario, and the use






of a structural default model to determine defaults consistent with the scenarios used.These changes do not require additional information beyond that already availableto direct supervisors. The framework has the potential to turn existing CCP-specificstress test results into a system-wide stress test if supervisors of different CCPs arewilling to collaborate and share the stress test results provided by CCPs overseenby them. This collaboration could be straightforward within various jurisdictions; forexample, in Europe, ESMA supervises, via various supervisory colleges, all EuropeanCCPs. To combine stress test information across jurisdictions and conduct a globalsupervisory stress test, however, international agreements may be needed.


The author reports no conflicts of interest. The author alone is responsible for thecontent and writing of this paper. The views expressed are those of the author anddo not necessarily reflect the views of the Office of Financial Research or the USDepartment of the Treasury.

ACKNOWLEDGEMENTS

I would like to thank Randall Dodd, Greg Feldberg, Samim Ghamami, Paul Glasser-man, Matt Pritsker, Stacey Schreft and an anonymous referee for comments andsuggestions, as well as Michelle Farrell for her excellent assistance in creatingFigure 2.

REFERENCES

Alfaro, R., and Drehmann, M. (2009). Macro stress tests and crises: what can we learn?Quarterly Review, December, pp. 29–41. Bank for International Settlements.

Bookstaber, R., Cetina, J., Feldberg, G., Flood, M., and Glasserman, P. (2013). Stresstests to promote financial stability: assessing progress and looking to the future.WorkingPaper, Office of Financial Research.

Borio, C., Drehmann, M., and Tsatsaronis, K. (2012). Stress-testing macro stress testing:does it live up to expectations? Working Paper 369, Bank for International Settlements.

Campbell, S. D., and Ivanov, I. (2016). Empirically evaluating systemic risks in CCPs:the case of two CDS CCPs. Working Paper, Social Science Research Network (https://doi.org/10.2139/ssrn.2841076).

Committee on Payments and Market Infrastructures–Board of the International Organi-zation of Securities Commissions (2017). Framework for supervisory stress testing ofcentral counterparties. Report, Bank for International Settlements.

Committee on Payment and Settlement Systems–Technical Committee of the Interna-tional Organization of Securities Commissions (2012). Principles for financial marketinfrastructures. Report, Bank for International Settlements. URL: http://bit.ly/1mcqA8x.





54 S. Tompaidis

Commodity Futures Trading Commission (2016). CFTC staff issues results of supervisorystress test of clearinghouses.Press Release 7483-16, November 16, CFTC.URL:https://bit.ly/2HXsAh0.

Dees, S., Henry, J., and Martin, R. (2017). STAMP€: stress-test analytics for macropru-dential purposes in the euro area. Report, European Central Bank.

Drehmann, M. (2009). Macroeconomic stress testing banks: a survey of methodologies. InStress Testing the Banking System: Methodologies and Applications, Quagliariello, M.(ed). Cambridge University Press (https://doi.org/10.1017/CBO9780511635618.005).

European Securities and Markets Authority (2016). ESMA publishes results for EU centralcounterparties stress test. Press Release, April 29, ESMA. URL: https://bit.ly/2HUxiyq.

Glasserman, P., Kang, C., and Kang, W. (2015). Stress scenario selection by empiricallikelihood. Quantitative Finance 15(1), 25–41 (https://doi.org/10.1080/14697688.2014.926019).

Hastie, T., Tibshirani, T. R., and Friedman, J. (2009). The Elements of Statistical Learning:Data Mining, Inference, and Prediction, 2nd edn. Springer (https://doi.org/10.1007/978-0-387-84858-7).

Henry, J., and Kok, C. (2013). A macro stress testing framework for assessing systemicrisks in the banking sector. Occasional Paper 152, October, European Central Bank.

Norman, P. (2011). The Risk Controllers: Central Counterparty Clearing in GlobalizedFinancial Markets. Wiley.

Pritsker, M. (2012). Enhanced stress testing and financial stability. Working Paper, SocialScience Research Network (https://doi.org/10.2139/ssrn.2082994).






Forum Paper

One for my baby (and one more for the road):incentives, default waterfalls and centralcounterparty skin-in-the-game

Rebecca Lewis and John McPartland

Economic Research, Federal Reserve Bank of Chicago, 230 S. LaSalle, Chicago, IL 60604, USA;emails: [email protected], [email protected]

(Received April 12, 2017; revised July 14, 2017; accepted December 6, 2017)

ABSTRACT

In this paper, we argue that both for-profit central counterparties (CCPs) and theirclearing members should contribute to the default waterfall, with a CCP’s two con-tributions coming directly before and directly after the tranche of clearing membercontributions. This arrangement encourages the CCP and its clearing members tofollow through on their responsibilities while balancing the interests of the CCP, itsclearing members and the public. We argue that, in addition to affecting the incen-tives of a CCP’s shareholders, the inclusion of CCP capital in the default waterfallaffects the incentives of its clearing members, as do the CCP contribution’s place-ment and size. CCPs and their regulators should determine the appropriate size of aCCP’s waterfall contributions, taking into account the problems that could arise fromcontributions that are too small or too large. We conclude with a brief examination ofdefault waterfalls at mutualized CCPs, arguing that, in most cases, they do not need aCCP capital contribution to align incentives, though it could still serve to reduce theinconvenience of replenishing a waterfall contribution after a modest default loss.

Keywords: central counterparties (CCPs); CCP capital; skin-in-the-game; default fund; defaultwaterfall.

Corresponding author: R. Lewis Print ISSN 2049-5404 j Online ISSN 2049-5412© 2018 Infopro Digital Risk (IP) Limited







56 R. Lewis and J. McPartland

1 INTRODUCTION

Central counterparties (CCPs) manage counterparty risk in cleared markets. Clear-ing mandates implemented after the 2007–8 financial crisis increased the systemicimportance of CCPs, which, in turn, increased policy makers’ focus on CCP risk man-agement. A fundamental risk facing CCPs is that of a clearing member default. CCPsaddress default risk through margin systems, default management procedures and adefault waterfall composed of funded and unfunded resources. The default waterfalloften includes contributions from clearing members as well as a contribution fromthe CCP, known in the industry as CCP “skin-in-the-game”.

In this paper, we argue that a CCP’s default waterfall design should align theinterests of the CCP, its clearing members and the broader public while ensuring thateach party follows through on its responsibilities as a CCP owner, user or supervisor.A for-profit CCP’s owners are responsible for ensuring that the CCP is well managed.They seek to earn the highest return on equity possible. Clearing members seek to clearat the lowest cost possible while still maintaining adequate protections against fellowclearing member defaults. They have a responsibility to aid default management,since the basic structure of a CCP relies on the mutualization of default risk acrossclearing members. The public, represented by the CCP’s regulator, has a responsibilityto supervise the CCP and an interest in ensuring financial stability.

Both a for-profit CCP and its clearing members should contribute to the defaultwaterfall. In this paper, we propose an ordering of the CCP and member-fundedtranches that encourages the CCP and its clearing members to follow through ontheir responsibilities while balancing the interests of the CCP, its clearing membersand the public. We discuss the factors that regulators and CCPs should consider whendetermining the size of the waterfall’s tranches, arguing that a CCP capital contributionthat is too small will fail to provide the CCP with an adequate incentive to effectivelymanage risk, while a contribution that is too large will diminish the incentives for theclearing members to aid default management. We conclude with a brief examinationof default waterfalls at mutualized CCPs.

This paper makes two main contributions to the debate over CCP capital contribu-tions to the default waterfall. First, we argue that, in addition to affecting the incentivesof a CCP’s shareholders, the inclusion of CCP capital in the default waterfall affectsthe incentives of its clearing members. We provide a fuller treatment of the ways inwhich CCP waterfall contributions affect both a CCP’s and its clearing members’incentives than previous papers and argue that the effects on incentives have implica-tions for the appropriate placement and size of CCP capital in the waterfall. Second,we argue that a for-profit CCP’s default waterfall should include two tranches of CCPcapital: a junior tranche before and a senior tranche after the prefunded waterfallcontributions of clearing members.





One for my baby (and one more for the road) 57

2 CURRENT TREATMENT OF DEFAULT WATERFALLS ANDCENTRAL COUNTERPARTY SKIN-IN-THE GAME

International guidelines address the appropriate size of CCP default waterfalls andrecommend the inclusion of CCP capital in the waterfall. While both the United Statesand the European Union (EU) have issued regulations implementing the guidelines ondefault waterfall size, only the EU requires that CCPs include a capital contributionin their default waterfall.

A review of current CCP skin-in-the-game policies demonstrates that CCP capitalcomprises a small proportion of the overall waterfall, with clearing member contribu-tions providing most of the resources available for absorbing a default loss. Generally,for-profit CCPs have one tranche of CCP capital in the waterfall located before theprefunded clearing member contributions.

Most of the literature on CCP waterfall contributions argues that the primary role ofthe CCP’s skin-in-the game is to encourage effective risk management. Some policymakers have also addressed the ways in which CCP waterfall contributions affectclearing members’ incentives.

2.1 Default waterfall regulations

International standards setters have issued guidance on CCP default waterfall policy.The Committee on Payment and Settlement Systems (CPSS) and International Orga-nization of Securities Commissions (IOSCO) published their Principles for FinancialMarket Infrastructures (PFMI) in 2012, and the “Resilience of central counterpar-ties (CCPS): further guidance on the PFMI” five years later (CPMI–IOSCO 2017).1

In the PFMI, CPSS–IOSCO recommend that all CCPs have sufficient resources intheir default waterfalls to withstand the default of the clearing member to which theCCP has the largest exposure: a “Cover 1” standard (CPSS–IOSCO 2012, p. 1). Theyalso recommend that complex or systemically important CCPs meet a “Cover 2”standard, with enough resources to withstand the simultaneous default of the twoclearing members to which the CCP has the largest exposures. The “further guid-ance” addresses the inclusion of CCP capital in the waterfall, stating that a CCPshould “determine and expose an amount of its own financial resources to absorblosses resulting from a participant default” (CPMI–IOSCO 2017). CPMI–IOSCOargue that the inclusion of CCP capital in the waterfall should increase confidencethat “the CCP’s design, rules, overall strategy and major decisions reflect appro-priately the legitimate interests of its participants and other relevant stakeholders”(CPMI–IOSCO 2017, p. 41).

1 The CPSS was renamed the Committee on Payments and Market Infrastructures (CPMI) in 2014.






Regulators have addressed default waterfall size and the inclusion of CCP capitalin the waterfall. The European Market Infrastructure Regulation (EMIR) covers theEU and requires that CCPs meet the Cover 2 standard recommended in the PFMI withprefunded resources (European Parliament and the Council of the European Union2012a). Under EMIR, CCPs must also contribute at least 25% of their minimum reg-ulatory capital to their default waterfall (European Parliament and the Council of theEuropean Union 2012b). They must place the contribution before the resources ofnondefaulting clearing members. The EU regulation dictates that the capital contri-bution be a set proportion of the CCP’s required regulatory capital, rather than a setproportion of the default waterfall (European Parliament and the Council of the Euro-pean Union 2012a). The use of this formula suggests that the regulation’s purpose isto enhance clearing members’ confidence in CCP risk management, not to providesubstantial loss absorbing capacity.

The US Commodity Futures Trading Commission (CFTC) and Securities ExchangeCommission (SEC) have each issued rules requiring that all CFTC- or SEC-regulatedCCPs meet a Cover 1 standard. The CFTC allows the use of prefunded resources andassessment powers, while the SEC allows only prefunded resources. Both the CFTCand SEC require certain systemically important CCPs to meet a Cover 2 standard usingprefunded resources (US Government Code of Federal Regulations 17 CFR 39.11,39.33, 240.17Ad-22(e)(4)). Neither has issued rules requiring CCPs to contributecapital to their default waterfalls.

2.2 Current default waterfall policies

A CCP’s default waterfall typically consists of the initial margin and default fundcontribution of the defaulting clearing member followed by a CCP capital contribu-tion. Next comes a mutualized portion of the waterfall to which all nondefaultingclearing members contribute. At the end of the waterfall, there are often unfundedclearing member assessments that the CCP can levy if necessary. If the CCP con-tributes capital to the waterfall, it is usually in a single tranche located just before theprefunded clearing member contributions. The CCP capital contribution tends to bea small part of the waterfall. The capital contributions of the major for-profit CCPsare summarized in Table 1.

As Table 1 shows, all CCPs listed include a capital contribution in their waterfalls.Eurex Clearing includes two CCP-funded tranches, with the second tranche beingused pari passu with clearing member assessments. While it only includes a singletranche of CCP capital, LCH links management compensation to the use of its capital,providing managers with an incentive to avoid a default loss large enough to reachthe CCP-funded tranche.






2.3 Literature on skin-in-the-game

Industry participants and policy makers generally agree that CCP skin-in-the-gamecan help to align the interests of a CCP’s shareholders with those of its clearing mem-bers. Several large clearing members have argued that, to best align CCP and clearingmember incentives, CCPs’ waterfall contributions should be a fixed percentage ofthe default waterfall or equal to the first, second or third largest clearing member’scontribution (JPMorgan Chase 2014; BlackRock 2014; De Leon et al 2014). Theseproposals recognize the effect of skin-in-the-game on CCPs’ incentives. However,proposals for such large increases in CCP waterfall contributions fail to take intoaccount the likely effects on clearing members’ incentives.

Some of the existing literature on CCP skin-in-the-game does address its effecton the incentives not only for CCPs but also for its clearing members. Carter andGarner (2015) discuss the effect of waterfall composition on the risk managementincentives of a for-profit CCP and its clearing members. They argue that, while CCPsmust commit enough capital that they have an incentive to prudently manage risk,“a very large increase in a CCP’s contribution to the default waterfall relative toclearing participant contributions may reduce participants’ incentives to take an activeinterest in the CCP’s broader risk management framework” (Carter and Garner 2015,p. 86). Cox (2015) discusses the effect of CCP skin-in-the-game on the incentivesfor clearing members to aid default management. He also points out that increasesin the size of CCP skin-in-the-game could lead to increases in clearing fees, andtherefore drive liquidity from cleared markets. He recommends that CCPs includetwo tranches of capital in their waterfalls. Murphy (2017) evaluates the trade-offsbetween the increased return on equity that CCPs earn when they decrease their skin-in-the-game and the decreased clearing use that may occur when a smaller skin-in-the-game contribution increases clearing members’ exposure to the CCP. Accordingto Murphy (2017, p. 10), “as SITG [skin-in-the-game] grows, so does CCP equity;but more SITG makes the DF [default fund] safer, and thus encourages more clearingand, hence, higher returns. There will be an optimal balance that maximizes ROE[return on equity]”.

2.4 Current risk management incentives

Throughout this paper, we shall address the ways in which the structure of a CCP’sdefault waterfall affects the incentives of clearing members and the CCP itself. Whileour focus is on the default waterfall, it is important to remember that regulations andCCP policies unrelated to the waterfall’s composition also affect the incentives forCCPs and their clearing members.

In later sections, we discuss CCPs’ incentives to take on additional risk at theexpense of clearing members. We suggest using CCP contributions to the default






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waterfall to combat these incentives. CCPs are also limited in taking on excessive riskby regulations. These include regulations setting out margin coverage requirementsthat ensure positions are adequately collateralized. CCP governance arrangementsfurther limit CCPs’ability to take on excessive risk. Many CCPs have risk committeesthat include clearing member representatives, providing clearing members with avoice in risk management and allowing them to constrain CCPs seeking to engage inoverly risky behavior.

In later sections, we also discuss clearing members’incentives to aid CCPs in defaultmanagement. We argue that CCP capital contributions to the default waterfall must beappropriately sized to avoid undermining these incentives. CCP default managementprocedures are often structured to reinforce clearing member incentives to help theCCP. One way in which some CCPs incentivize auction participation is throughjuniorization. Under this policy, the default fund contributions of clearing memberswho fail to submit bona fide bids at the auction are used before those of clearingmembers who participated in good faith.

3 OUR PROPOSED DEFAULT WATERFALL

In contrast to the current structure of most CCP default waterfalls, we propose thatwaterfalls include two separate tranches of CCP capital, one before and one after theprefunded clearing member contributions. We argue that CCP capital contributions tothe default waterfall affect the incentives facing not just the CCP, but also its clearingmembers. Policy makers and CCPs must take this into account when determining theappropriate placement for and size of the CCP-funded waterfall tranches.

After outlining our proposed waterfall, we turn to several aspects of the proposalthat require further discussion. First, we argue that to ensure CCPs and their clearingmembers fulfill their responsibilities toward one another, both should contribute tothe default waterfall. Second, we discuss the sizing of the CCP-funded tranches. Weargue that the role of CCP contributions is to align incentives, not to absorb substantiallosses. We note that, while a CCP capital contribution that is too small can distortthe incentives of the CCP, a contribution that is too large can distort the incentivesof the clearing members. We then provide a justification for our placement of theCCP-funded tranches, using its effects on clearing member and CCP incentives forsupport. Finally, we address the appropriate default waterfall structure at a mutualizedCCP.

3.1 Default waterfall at a for-profit CCP

Our proposed for-profit CCP’s default waterfall is illustrated in Figure 1. Thedefaulter’s resources (its initial margin and guarantee fund contribution) should be






FIGURE 1 Proposed default waterfall.

Proposed default waterfall

Defaulter's initial margin

Defaulter’s guarantee fund contribution

CCP-funded junior tranche

Clearing member contributions

CCP-funded senior tranche (pari passu with assessments)

Clearing member assessments

Funded

Unfunded

first in the waterfall. After the defaulter’s resources, there should be a junior trancheof CCP capital, followed by a tranche of clearing member contributions. Next thereshould be a senior tranche of CCP capital contributed pari passu with assessmentsfrom clearing members.

3.2 Why should both the CCP and clearing members contribute tothe default waterfall?

To understand why both a CCP and its clearing members should contribute to thedefault waterfall, imagine the incentives that would arise if this were not the case.

A for-profit CCP with a solely clearing-member-funded waterfall would have twomain problems. The CCP’s limited exposure to default risk could lead to more laxrisk management, with the CCP valuing return on equity over safety. The potentialfor less stringent risk management would also make it difficult for the CCP to cred-ibly demonstrate to its clearing members that it was fulfilling its risk managementresponsibilities.

A for-profit CCP with a solely CCP-funded waterfall would have several problems.At many CCPs, clearing members play an important role in risk management throughtheir representatives on the CCP’s risk committee. If clearing members had no capital






in the default waterfall, their incentives to monitor risk management at the CCP wouldbe significantly reduced. They might be willing to allow lower margining standardsor the admission of less creditworthy clearing members, since unless a default werelarge enough to lead to the failure of the entire CCP they would not bear any of itscost.

With no contributions to the waterfall, clearing members would be less inclined toaid the default management process. During a typical default management, the CCPliquidates the defaulter’s house positions and seeks clearing members willing to acceptthe defaulter’s clients. A prompt transfer of clients and liquidation of house positionson terms favorable to the CCP minimizes the default loss. If the CCP were responsiblefor absorbing all default losses, clearing members would be more reluctant to acceptclients or aid in liquidation. If they were to accept clients or bid for positions, theywould likely require terms less favorable to the CCP.

A CCP that provided all of the funds for its default waterfall would likely preferto tear up unmatched contracts at a price that preserved its shareholders’ capitalrather than pay the price necessary to find market participants willing to take over thedefaulter’s positions. Tear-up can be contagion transmitting, increasing the negativesystemic effects of a default. History bears this out: after the 1974 default of itslargest clearing member, the Caisse de Liquidation des Affaires et Marchandises,which lacked mutualized default resources, refused offers to rematch its book becauseacceptance would result in a loss for the CCP’s shareholders. Ultimately, the Caissede Liquidation failed (Bignon and Vuillemey 2016).

To raise enough capital to fund the entire waterfall, a CCP might have to raiseclearing fees. This, in turn, could increase systemic risk as market participants engagedin fewer risk-transfer transactions or searched out substitute products that were notsubject to the clearing mandate. If enough participants left the cleared market, theerosion in liquidity could make it more difficult to liquidate positions during a default,as Cox (2015) notes. The extent to which the increase in fees would lead to anincrease in clearing members’ overall costs is unknown, since the increased feesmight be partly offset by the elimination of default risk mutualization among clearingmembers.

A more fundamental problem with an entirely CCP-funded waterfall is that it failsto recognize the essential character of a CCP. As Coeuré (2015) argues, “CCPs arerisk poolers, not insurance providers”. It is the clearing members that bring the riskto the CCP, and it is the mutualization of default risk among the clearing membersthat allows the clearing model to function. According to Cox (2015, p. 65), CCPsare structured around the principle that “those who bring the risk shall bear it, [and]should they fail the rest must share it”. To change this would require a completerestructuring of the clearing industry.






3.3 How to size the waterfall tranches

Regulations currently set a minimum overall size for CCPs’ default waterfalls. Whatremains to be determined is the appropriate size of the CCP’s capital contributionto the waterfall. The incentives that would arise from an entirely CCP- or clearing-member-funded waterfall discussed in the previous section help guide our analysis.

A CCP’s capital contribution to the waterfall could be too small. Murphy (2017)suggests that, without a sufficient CCP capital contribution, clearing members mayperceive the risk of clearing to be too high and so seek to engage in over-the-counter(OTC) rather than cleared trades. A CCP that contributed little capital to the waterfallmight also lack sufficient incentives to manage default risk and minimize default loss.As Carter and Garner (2015, p. 85) argue,

the strength of [the risk management] incentive is determined by the value of theCCP’s resources at risk in the event of a participant default. That is, the CCP’sincentives for prudent risk management are likely to be optimized by requiring itsskin in the game to be a material portion of its own capital – and this would be trueirrespective of the size of the CCP’s skin in the game relative to the size of its totaldefault waterfall.

A CCP’s capital contribution to the default waterfall could be too large. A for-profitCCP must earn a return on capital. If it has to commit more capital to the waterfall, itwill have to seek returns on that capital, likely through increased clearing fees. Thiscould create a disincentive to clear trades, increasing systemic risk. A CCP’s capitalcontribution should also not be so large that its use threatens the CCP’s survival. AsCox (2015, p. 68) argues, “‘front line’ CCP capital should be large enough if lostto represent a serious ‘black eye’ for the shareholders and management; however, itshould not be so large as to endanger the CCP’s continued existence”.A junior trancheof CCP capital that is too large could act as a disincentive for clearing membersto help manage clearing member defaults. Since a junior tranche absorbs defaultlosses before the clearing members’ contributions to the waterfall, a larger juniortranche decreases the chance that a default loss will require funds from nondefaultingclearing members. As this probability decreases, the incentive to participate in defaultmanagement decreases.

It is impossible to state the exact amount of CCP capital that should be in all defaultwaterfalls. Since not all CCPs are the same, “articulating a ‘one-size-fits-all’ standardmay well miss the mark for many CCPs” (Cox 2015, p. 67). As Coeuré (2015) argues,since

there is a broad diversity in the risk profiles of CCPs … there may be no single bestsolution for addressing concerns related to CCP safety. Optimal solutions may varyaccording to the business model of the CCP … [and] according to the CCP’s riskprofile.... Regulators may wish to keep a certain degree of flexibility when theymake final recommendations.






Each CCP, along with regulators representing the public interest, should weigh theconsiderations outlined above and determine the appropriate amount of CCP capitalto include in the default waterfall. Discussions about the size of a CCP’s capitalcontribution to a default waterfall must also recognize that the contribution is primarilyabout aligning incentives and not absorbing the majority of a default loss.

3.4 Placement of CCP-funded tranches

Since a defaulting clearing member has, by definition, failed to meet its obligations tothe CCP, its resources should be located earlier in the default waterfall than those ofnondefaulting clearing members. We have argued that the two tranches of CCP capitalshould be located immediately before and immediately after the clearing members’prefunded contributions to the default waterfall. We now explain the justification forthe placement of each CCP-funded tranche in turn.

A junior tranche of CCP capital located before the clearing member contributionsserves several purposes. Since it is placed high enough in the waterfall to be at riskduring a default, the junior tranche provides the CCP with an incentive to fulfill itsrisk management responsibilities. The junior tranche can also serve as a “nuisancedeductible”, allowing clearing members to avoid replenishing their contributions tothe default waterfall after the default of a modestly sized clearing member. The pro-cess of replenishing clearing member contributions can be inconvenient for both theCCP and individual clearing members. A junior tranche of CCP capital increases thedefault loss required to reach the clearing member contributions and thus to requirereplenishment.

The final role of the junior tranche is to provide an incentive for clearing mem-bers to participate in the default management process. During default management,CCPs liquidate the open positions of a defaulting clearing member, often through anauction. A junior tranche will act as “auction bait”, encouraging clearing membersto participate. Since default waterfall resources cover auction losses, clearing mem-bers have an incentive to keep the auction loss small enough that resources from themutualized portion of the waterfall are not required. The presence of a junior trancheincreases the size of the auction loss necessary to reach mutualized default resources.This allows clearing members to place better bids than they would in its absence, cre-ating an incentive to meaningfully participate in the auction. As noted in the previoussection, the junior tranche should never be so large that it ceases to act as bait andinstead simply subsidizes the entire auction.

CCPs should place a senior tranche of capital following the prefunded clearingmember contributions, to be contributed pari passu with clearing member assess-ments. A senior tranche in this location gives the CCP an incentive to appropriatelysize the previous portion of the waterfall in order to prevent a default loss from






reaching its second tranche of capital. After a clearing member default, if the seniortranche is not used, it can provide funds for replenishing the junior tranche, helpingthe CCP to maintain the market’s confidence in its financial stability.

During a large default, a senior tranche will provide a way for the CCP to demon-strate its commitment to recovery. Since a for-profit CCP seeks to make money fromits clearing services, sharing in the loss when it is making large demands upon itsclearing members will help to encourage clearing member participation even duringa particularly costly default. A senior tranche will also reduce asymmetry in the dis-tribution of tail risk; without a senior tranche of CCP capital, clearing members alonefund the portion of the waterfall that covers losses in extreme market conditions.

We recommend that the senior tranche be used pari passu with a CCP’s assessmentpowers, with funds from the senior tranche and clearing member assessments con-tributed together until the loss is covered or the available resources are exhausted. Thiswill ensure that, throughout the assessment process, the CCP participates alongsideclearing members.

Some CCPs might wish to place their senior tranche after assessments and reducethe likelihood of its use. While we see this as preferable to having no senior tranche, itprovides fewer benefits than a senior tranche located pari passu with assessments. Asenior tranche located after assessments would still be available to replenish the juniortranche after a default loss. It would be less effective at convincing clearing membersof the CCP’s willingness to share the pain of assessments, since the CCP would onlyuse the senior tranche after exhausting its assessment powers. Some might arguethat a senior tranche at the very end of the default waterfall would increase marketparticipants’ confidence in a CCP’s ability to continue to function even in times ofextreme market stress. This argument fails to recognize that the market conditionsrequired to reach the assessment portion of a CCP’s default waterfall are likely to beso dire that a small amount of CCP capital at the end of the waterfall is unlikely tomake a difference to participants’ confidence, or lack thereof.

3.5 Default waterfalls at mutualized CCPs

So far, we have discussed for-profit CCPs’ default waterfalls. At for-profit CCPs,CCP capital contributions matter since the owners and users are distinct and so haveresponsibilities toward one another and potentially divergent interests. Some CCPs aremutualized, which means that they are owned by the clearing members and exchangesthat use their services.

At some mutualized CCPs, the owners and users of a mutualized CCP are largely thesame. As a result, most of the justifications for a CCP capital contribution discussedabove no longer hold; regardless of whether it is classified as a clearing member






contribution or CCP contribution to the waterfall, the ultimate source of the funds isthe same.

There is one exception to this argument that might lead some mutualized CCPs toinclude a junior tranche of CCP capital in their default waterfall.A junior tranche couldstill serve as a nuisance deductible. As discussed above, a junior tranche can preventsome default losses from reaching the mutualized portion of the default waterfall,allowing the CCP and its clearing members to avoid the process of replenishingclearing member contributions to the waterfall. At a mutualized CCP, the clearingmembers would have to replenish any funds used from the junior tranche but coulddo so from the CCP’s retained earnings, rather than capital calls on clearing members.Replenishing the junior tranche from retained earnings would likely take longer thanreplenishing it through capital calls, so this strategy may not work if it meant thedefault waterfall would fail to meet regulatory size requirements for the replenishmentperiod.

Some mutualized CCPs are owned by only some of their clearing members, orare mainly exchange-owned. However, since mutualized CCPs are generally not for-profit, there is no incentive for the owners to take excessive risks at the expenseof clearing members. As a result, even when the users and owners do not perfectlyoverlap, there is no need to align incentives by including a CCP capital in the defaultwaterfall (though a junior tranche may still be useful as a nuisance deductible). If amutualized CCP owned by only a few clearing members began to operate as a for-profit entity, a CCP capital contribution would become necessary. In that case, theCCP would function more as a for-profit CCP with a limited number of shareholdersthan as a traditional mutualized CCP.

Mutualized CCPs have varying policies on CCP capital contributions to their defaultwaterfalls. The Options Clearing Corporation (OCC) does not include any CCP capi-tal in its default waterfall, but its rules state that the “use of retained earnings by OCCis discretionary at any stage of the default” (Options Clearing Corporation 2016, p. 5).The Depository Trust and Clearing Corporation (DTCC) has two default waterfalls,both of which include a CCP capital contribution before the prefunded clearing mem-ber contributions to the waterfall (Depository Trust and Clearing Corporation 2015).EuroCCP does not include a CCP capital contribution in its waterfall. It does havean equity capital layer that can be used once the default fund is exhausted. However,the “scheme is designed not to have to call upon” that layer (see https://euroccp.com/content/risk-management-and-financials).

4 CONCLUSION

In this paper, we argued that both for-profit CCPs and their clearing members shouldcontribute to the default waterfall, with a CCP’s two contributions coming directly






before and directly after the tranche of clearing member contributions. CCPs and theirregulators should determine the appropriate size of the CCP’s waterfall contributions,taking into account the problems that would arise from contributions that are toosmall or too large. Most mutualized CCPs do not need a CCP capital contribution toalign incentives, though in some cases it could still serve to reduce the inconvenienceto clearing members and CCPs associated with replenishing a waterfall contributionafter a modest default loss.

As CCPs play an increasingly important role in global financial markets, the impor-tance of effective risk management at CCPs will also increase. At for-profit CCPs,effective risk management requires an appropriately sized and structured defaultwaterfall that encourages a CCP and its clearing members to follow through on theirresponsibilities while balancing the interests of the CCP, clearing members and thepublic.


The authors report no conflicts of interest. The authors alone are responsible for thecontent and writing of the paper. The views expressed in the paper are the authors’own and do not reflect the policy of the Federal Reserve Bank of Chicago or theFederal Reserve Board.

ACKNOWLEDGEMENTS

The authors thank Robert Cox, Chris Jones, Ketan Patel, Max Rong and RobertSteigerwald for their helpful comments and assistance.

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Volume 6/Number 1

RESEARCH PAPERSEstimating “hedge and auction” liquidation costs in centralcounterparties: a closeout risk approach 1–27Luis A. B. G. Vicente, Fernando V. Cerezetti and Alan De Genaro

Nondefault loss allocation at central counterparties 29–44Rebecca Lewis and John McPartland

Nonmonotonic trade-offs of tiering in a large-value payment system 45–78Carlos A. Arango-Arango and Freddy H. Cepeda-López

FORUM PAPERCentral counterparty recovery and resolution: the Europeanperspective 79–106H. Huhtaniemi and M. Peters

Volume 6/Number 2/3

RESEARCH PAPERSFMIC 2 special issue introduction: a policy view on developments inthe field of financial market infrastructures 1–20Biliana Alexandrova-Kabadjova, Evangelos Benos, Jo Braithwaite,Jorge Cruz Lopez, Ronald Heijmans, Mark Manning,David Murphy and Francisco Rivadeneyra

The threat of privacy 21–30Charles M. Kahn

The impact of de-tiering in the United Kingdom’s large-value paymentsystem 31–62Evangelos Benos, Gerardo Ferrara and Pedro Gurrola-Perez




The absence of evidence and the evidence of absence: an algorithmicapproach for identifying operational outages in TARGET2 63–91Marc Glowka, Jan Paulick and Inga Schultze

SPEI’s diary: econometric analysis of a dynamic network 93–119M. A. Gavilan-Rubio and Biliana Alexandrova-Kabadjova

Central counterparties and systemic stability 121–129Marc Bayle de Jessé

When do central counterparties enhance market stability? 131–162David Marshall, Ivana Ruffini and Dominic Anene

Risk mutualization and financial stability: recovering and resolvinga central counterparty 163–185Radoslav S. Raykov

Central counterparty resolution: an unresolved problem 187–206Manmohan Singh and Dermot Turing

Distributed ledger technology in payments, clearing and settlement 207–249David Mills, Kathy Wang, Brendan Malone, Anjana Ravi,Jeff Marquardt, Clinton Chen, Anton Badev, Timothy Brezinski,Linda Fahy, Kimberley Liao, Vanessa Kargenian,Max Ellithorpe, Wendy Ng and Maria Baird

Volume 6/Number 4

RESEARCH PAPERSA CCP is a CCP is a CCP 1–18Robert T. Cox and Robert S. Steigerwald

Freeriding on liquidity in the Colombian large-value payment system 19–40Constanza Martínez and Freddy Cepeda

Measuring system-wide resilience of central counterparties 41–54Stathis Tompaidis

FORUM PAPEROne for my baby (and one more for the road): incentives, defaultwaterfalls and central counterparty skin-in-the-game 55–70Rebecca Lewis and John McPartland




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