Central counterparty capitalization and misaligned incentives


BIS Working Papers

No 767


Central counterparty capitalization and misaligned incentives

by Wenqian Huang


Monetary and Economic Department

February 2019


JEL classification: G01, G12, G21, G22

Keywords: Central counterparties (CCPs), capital requirement, financial stability


This publication is available on the BIS website (www.bis.org).


© Bank for International Settlements 2017. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated.

ISSN 1020-0959 (print)

ISSN 1682-7678 (online)


Central counterparty capitalization and misaligned incentives

by Wenqian Huang

First draft: December 14, 2016

This draft: February 7, 2019



Financial stability depends on the effective regulation of central counterparties (CCPs), which must take account of the incentives that drive CCP behavior. This paper studies the incentives of a for-profit CCP with limited liability. It faces a trade-off between fee income and counterparty credit risk. A better-capitalized CCP sets a higher collateral requirement to reduce potential default losses, even though it forgoes fee income by deterring potential traders. I show empirically that a 1% increase in CCP capital is associated with a 0.6% increase in required collateral. Limited liability, however, creates a wedge between its capital and collateral policy and the socially optimal solution to this trade-off. The optimal capital requirements should account for clearing fees.

Keywords: central counterparties (CCPs), capital requirement, financial stability.

JEL classifications: G01, G12, G21, G22.

  • Wenqian Huang, Bank for International Settlements, Centralbahnplatz 2, CH-4002 Basel, tel +41 612 808 000, Wenqian.Huang@bis.org. The paper won the Young Economist Prize at the 2017 ECB Sintra Forum on Central Banking. The author is grateful for helpful comments from Morten Bech, Evangelos Benos, Markus Brunnermeier, Stijn Claessens, Jorge Cruz Lopez, Gerardo Ferrara, Ingo Fender, Nathan Foley-Fisher, Pedro Gurrola-Perez, Bo Hu, David Lando, Mark Manning, Albert Menkveld, David Murphy, Loriana Pelizzon, Cristina Picillo, Bernd Schwaab, Nikola Tarashev, Elod Tak ¨ ats, Michalis Vasios, Marius ´ Zoican, and from conference participants at the 2017 European Finance Association annual meeting, the 2017 ECB Sintra Forum on Central Banking, the Fifth International Credit Risk Conference, and the 2019 American Economic Association annual meeting, as well as from seminar participants at the Bank of England, the Bank for International Settlements, Chicago Fed, Copenhagen Business School, Goethe University Frankfurt, Norwegian School of Economics, Rotman School of Management, Tinbergen Institute and Vrije Universiteit Amsterdam. The views expressed here are those of the author and do not necessarily coincide with those of the Bank for International Settlements.,



Central counterparties (CCPs) are systemically important. First, the outstanding positions cleared by CCPs are enormous. For over-the-counter (OTC) interest rate derivatives, the notional amount of centrally cleared contracts was USD 366 trillion in June 2018, accounting for 76% of global outstanding positions (BIS, 2018). Second, CCPs and other financial institutions are highly inter­connected via clearing membership, custodianship and credit relationships (BCBS-CPMI-IOSCO- FSB, 2017, 2018).

Unfortunately, CCPs are not invulnerable and their failure could threaten financial stability. There were several clearinghouse failures in the 1970s and 1980s, which led to market shutdowns. More recently, stress in the European repo market in 2016 suggests that market participants did indeed price in the probability of a CCP failure (Boissel et al., 2017). In September 2018, a single trader's default wiped out two-thirds of the default fund of Nasdaq Clearing, a CCP that the Financial Stability Board (FSB) identifies as systemically important.

For-profit CCPs have incentives that are misaligned with financial stability. Since the demu­tualization trend in 1990s, many CCPs have operated as for-profit publicly listed financial firms. These include the Chicago Mercantile Exchange (CME) in the U.S. and Eurex Clearing in Europe. There are public debates as to whether these CCPs have enough capital to align incentives appro­priately (see, e.g., Bignon and Vuillemey, 2018; Giancarlo and Tuckman, 2018). Furthermore, clearing members with exposures to CCPs have called for them to hold more capital, arguing that CCPs are not adequately incentivized to manage risk (see, e.g., Financial Times, 2014).

This paper investigates CCP capital and incentives. In particular, it explains how profit maxi­mization and limited liability give rise to incentives for CCPs that are misaligned from the view­point of a benevolent social planner. The paper further shows how optimal capital requirements might be designed to correct the misaligned incentives. Finally, it provides new empirical evidence that more CCP capital leads to more prudent risk management.

The model has three types of agent: risk-averse protection buyers, risk-neutral protection sell­ers, and a risk-neutral for-profit CCP. Each risk-averse buyer is endowed with a unit of a risky asset, such as a bond. To hedge the risk, buyers purchase insurance from sellers via a derivatives contract, such as a credit default swap (CDS), that is cleared by the CCP (Biais, Heider, and Ho- erova, 2012, 2016). Buyers and sellers are clearing members of the CCP. They trade only when there are utility gains from trading net of clearing costs. The total welfare surplus is equal to the CCP's expected utility plus the traders' utility gains from trading.

Sellers have heterogeneous capacities to reduce their losses in the bad state when the risky asset suffers a negative shock (Perez Saiz, Fontaine, and Slive, 2013). While the distribution of the loss-reduction capacity is common knowledge, the CCP does not observe individual sellers' loss-reduction capacities. As a result, the CCP sets a uniform collateral requirement, i.e., an initial margin requirement, for all sellers. In practice, CCPs generally seek to overcome the asymmet­ric information problem by (i) setting relatively strict membership requirements and (ii) charging credit add-ons to reflect the creditworthiness of clearing members. Although such measures may reduce the asymmetry of information between CCPs and members, they are not perfect. As the recent Nasdaq Clearing episode shows, CCPs can substantially misjudge the creditworthiness of their clearing members.

A seller defaults strategically when his out-of-the-money position, net of his loss reduction, is larger than the collateral he has posted. In other words, a seller’s loss-reduction capacity determines his creditworthiness as a counterparty.

The for-profit CCP in the model has three important characteristics. First, it always has a matched book and is not directly exposed to market risk (Cox and Steigerwald, 2017). Instead, it is exposed to counterparty credit risk because it needs to cover the losses when some traders default. Second, the CCP relies on a so-called default waterfall to mutualize counterparty credit risk (Duffie, 2015). In case of defaults, the waterfall specifies different layers of prefunded resources contributed by the defaulting traders, the CCP and the non-defaulting traders. Without capital regulation, as is currently the case, the CCP chooses how much capital to contribute to the default waterfall, which is often called skin-in-the-game. Third, the CCP’s profit comes solely from fees that are set exogenously per unit of cleared contracts. The CCP’s expected utility is the volume-based fee income, minus the capital cost and the potential loss of its capital as a result of defaults by clearing members.

Main findings. The model shows that a CCP with more capital requires more collateral from its clearing members. A higher collateral requirement lowers the default rate as well as the loss-given- default. This does, however, cause profitable trades to be forgone, hence reducing fee income. When a CCP has a higher level of capital, it is more concerned about the losses from counterparty risk that eats into this capital. Hence, it will set a higher collateral requirement to disincentivize defaults.

High skin-in-the-game has an ambiguous impact on traders’ utility gains. On the one hand, since collateral is costly, a higher level of CCP capital means that traders must bear higher collateral costs. In fact, CCPs use this point to argue against high skin-in-the-game (see, e.g., LCH, 2014). On the other hand, a better-capitalized CCP is less likely to impose losses on surviving traders (CPMI-IOSCO, 2012). Depending on which channel dominates, increasing CCP skin-in-the-game can increase or decrease the utility gains from trading.

A key insight of the model is that, if a for-profit CCP faces no capital requirements, the amount of capital it chooses to hold is lower than the socially optimal level. As the CCP is protected by limited liability, its expected utility decreases with the capital cost and the potential loss on this capital. As a result, without capital requirements, the CCP chooses zero capital. In this case, when the negative shock is realized, the CCP finds itself with insufficient prefunded resources and becomes insolvent. This means that buyers are not fully insured, leading to a loss of economic efficiency.

The socially optimal capital requirement for a for-profit CCP needs to take into account the per-unit clearing fee. The higher the fee, the higher the temptation for a for-profit CCP to increase trading volume. When the fee is below a certain threshold, a high capital requirement is effective ex ante in incentivizing the CCP to eliminate the counterparty credit risk it is exposed to. The CCP does so by increasing its collateral requirement which reduces trading volume. When the fee is above the threshold, the CCP maximizes its trading volume by charging its clearing members a low collateral requirement. In this case, the capital requirement cannot rule out defaults, but it serves as loss-absorbing capacity ex post in the event of defaults. Although the optimal capital requirement can avoid the dead-weight loss of an insolvent CCP, it is worthwhile pointing out that it does not achieve the first-best outcome because of the costliness of the collateral and capital.

The model also allows me to study the optimization problem of a CCP that is owned by all clearing members, and hence maximizes the total welfare surplus (instead of its own expected utility as in the case of a for-profit CCP). In line with Cox and Steigerwald (2016) who study CCPs with different ownership structures, I call this type of CCP “user-owned”. Compared with a for-profit CCP, a user-owned one holds more capital and sets a lower collateral requirement. In real operations, user-owned CCPs are owned mainly by a small number of large clearing members. Hence, there could be misaligned incentives between the larger members (who are CCP owners) and the smaller members (who have no say in deciding CCP capital and collateral policy). As my aim is to contrast a for-profit CCP against a user-owned CCP, I assume away this layer of misaligned incentives.

Finally, the paper provides empirical evidence from CCP quantitative disclosure data (CPMI- IOSCO, 2015) and CCP ownership information from public sources. In total, there are 16 CCPs at the group level and 44 CCPs at the entity level, which captures the majority of the clearing industry. The data are at a quarterly frequency and range from 2015 Q3 to 2017 Q4.

The empirical results support the theoretical model. First, panel regressions show that there is a significantly positive relationship between CCP skin-in-the-game and required initial margin for the for-profit CCPs in the sample. A 1% increase of CCP skin-in-the-game is associated with an increase of more than a 0.6% in the required initial margin. Second, ownership structures matter for CCP skin-in-the-game. The for-profit CCPs in the sample have significantly lower skin-in-the- game than the user-owned ones.

Relevant literature. Central clearing has three main features: multilateral netting across coun­terparties, a central data warehouse of outstanding position information, and mutualization of coun­terparty credit risk. While the first two features are reminiscent of other payment and settlement systems, the mutualization feature is unique to CCPs.

This paper contributes to the fast-growing literature on incentives and risks resulting from the mutualization of counterparty credit risk. The basic setup is similar to Biais, Heider, and Ho- erova (2012) and Biais, Heider, and Hoerova (2016). But the key economic frictions are different. This paper focuses on a for-profit CCP's incentives while theirs study traders' risk-shifting in­centives. Biais, Heider, and Hoerova (2012) explain the risk allocation implications of central clearing. Their model suggests that, although central clearing brings diversification benefits by mutualizing counterparty credit risk, a CCP should not offer full insurance against counterparty credit risk due to moral hazard problems. Central clearing reduces traders' incentives to acquire information and monitor counterparty credit risk, leading to a higher aggregate risk. Biais, Heider, and Hoerova (2016) show that margin requirements, together with central clearing, can preserve the risk-prevention incentives by inducing the optimal level of risk monitoring and can exploit the mutualization benefits of risk-sharing.

In this paper, the key frictions are that (i) the heterogeneous loss-reduction capacities (i.e., the counterparty credit risk) of individual sellers are not observable to the CCP; and (ii) the for-profit CCP does not internalize its impact on the traders’ utilities. The modeling of heterogeneity is built on Perez Saiz, Fontaine, and Slive (2013). However, their focus is on the impact of central clearing on dealers’ competition and profits. Koeppl (2013) also studies the unobservable counterparty credit risk in the context of central clearing. In his setup, the CCP does not chase profit but minimizes counterparty risk. Hence, the incentives of the CCP are very different from those of the for-profit CCP in this paper.

This paper explains how using the prefunded financial resources sequentially (i.e., following the default waterfall) can create intertwined incentives between the CCP and the traders, adding to the literature on CCP prefunded financial resources, which emphasizes the overall loss allocation rules (Elliott, 2013; Cumming and Noss, 2013), the adequacy of the default fund (Murphy and Nahai-Williamson, 2014; Capponi, Wang, and Zhang, 2018), and the roles of CCP skin-in-the- game (Carter, Hancock, and Manning, 2016; Carter and Garner, 2016; Murphy, 2016).

The existing literature considers many critical aspects of central clearing but, fundamentally, previous researchers have assumed that CCPs are benevolent organizations, which could be true in some cases. However, given that many for-profit CCPs are publicly listed financial firms, one should not overlook their incentives. This paper takes a different approach from the literature, as it explicitly models CCPs' for-profit incentives. It also provides empirical evidence that a higher level of CCP capital is associated with a higher required collateral.

The remainder of this paper is as follows. Section 2 introduces the model. Section 3 focuses on the misaligned incentives for a for-profit CCP. Section 4 analyses the optimal capital requirement of the for-profit CCP. Section 5 studies the case of a user-owned CCP. Section 6 provides empirical evidence from CCP quantitative disclosure data. Section 7 concludes.

  1. Model

    1.1 Model setup

The two-period model has three types of agent: protection buyers, protection sellers and a for- profit CCP. At t = 0, the CCP chooses its capital and sets the collateral requirement. Observing the CCP's capital and the collateral requirement, the buyers and sellers decide whether to trade a standard protection contract that is cleared by the CCP. At t = 1, uncertainty is resolved and
payoffs are realized. All the variables are summarized in Appendix A.

Protection buyers. There is a unit mass of homogeneous protection buyers who are risk-averse. They are endowed with one unit of a risky asset at t = 0. The asset has a random return в at t = 1. в can take on two values: в (> 0) in the good state with probability п and 0 in the bad state with probability 1 - п.

The risk-averse buyers purchase insurance from sellers via a protection contract. The contract has zero-mean and provides full insurance to the buyers. In other words, the contract specifies that the buyers pay the sellers (1 - п)в in the good state; and the sellers pay the buyers пв in the bad state.

Protection sellers. There is a unit mass of heterogeneous protection sellers who are risk-neutral and have limited liability. They are endowed with loss-reduction capacity in the bad state. The capacity to reduce loss in the bad state varies across sellers. Let rj denote the loss-reduction capacity of seller j. This means that, for each protection contract, seller j can reduce his loss by rjnO in the bad state. Instead of paying пв to his buyer

Image yh6n

in the bad state, seller j pays (1 - rj)n0. For simplicity, I assume that rj is uniformly distributed on an interval of (0,1). The distribution of rj is common knowledge; but the individual seller’s rj is observable to the buyers but not observable to the CCP (elaborated below). The assumption of heterogeneous loss-reduction capacity is not far from reality. Dealers in derivatives markets normally have their own specialty in managing their position risk (Perez Saiz, Fontaine, and Slive, 2013).

Each protection buyer is randomly matched with one protection seller who has one unit of contract to sell. However, matching does not guarantee trading. A buyer and a seller decide to trade or not depending on their utility improvement from trading (and clearing). If their utility improvement from trading is positive, they will trade and Nash-bargain to split the positive utility improvement. As long as both buyers and sellers have positive bargaining power, their individual utility improvement is positive when the joint utility improvement is positive. Hence, the trading volume between a buyer and a seller can be either zero or one, depending on the joint utility improvement for this pair of traders.

CCP. The contract is required to be centrally cleared. A representative competitive CCP clears all the trades. Through novation, the protection contract is split into two contracts: one is between the protection buyer and the CCP, and the other is between the protection seller and the CCP. If traders default, they default on the CCP.

The CCP demands collateral to disincentivize defaults. As specified by the protection contract, the buyers are out-of-the-money in the good state and the sellers in the bad state. Since the buyers receive a high payoff from the risky asset in the good state, they can settle the out-of-the-money positions smoothly. Although the sellers can reduce the downside risk, their loss-reduction ca­pacities are not large enough to settle the out-of-the-money positions (rj < 1). Hence, the sellers have incentives to default in the bad state. To protect itself from the sellers’ defaults, the CCP requires that the sellers post collateral. Since individual sellers’ loss-reduction capacities are not observable to the CCP, it sets a uniform collateral requirement based on the distribution of sellers’ loss-reduction capacities. Hence, a seller with loss-reduction capacity rj will not default when his collateral with the CCP is larger or equal to his loss from the out-of-the-money position. Let c denote the collateral requirement for each unit of outstanding position. The per-unit collateral cost borne by sellers is 8.

Apart from the collateral requirement, the CCP has other prefunded financial resources: the default fund and the CCP’s capital. Each seller’s default fund contribution is proportional to his collateral, i.e., ac, where a is an exogenous parameter.11 Without capital regulation, the CCP chooses its own capital K. The per-unit capital cost borne by the CCP is (Duffie, 2015):

  1. the collateral contributed by defaulting sellers;
  2. the default fund contribution by defaulting sellers;
  3. the CCP’s capital K;
  4. the default fund contributed by non-defaulting sellers.

When the default fund contributed by the non-defaulting sellers is used to cover default losses, the non-defaulting sellers share the losses evenly. Let d denote the default fund losses of each non-defaulting seller. At the end of the default waterfall, the remaining loss will be borne by the buyers evenly, meaning the buyers are only partially insured. Let w denote the wedge between the required payment (specified by the contract) and the actual payment.

The CCP is a risk-neutral and for-profit financial firm. The CCP’s income comes from a volume-based fee. Both the buyers and sellers need to pay f for each unit cleared. The fee level f is exogenous as the CCP is a price-taker. Instead of increasing the fee level, the CCP can increase the trading volume by changing the collateral requirement c, since the high collateral cost could deter some sellers from trading. The CCP is a limited liability entity, which means the maximum loss that it needs to cover will not exceed its own capital. Let L denote the total default loss that needs to be borne by the CCP, and v denote the trading volume. The risk-neutral CCP maximizes the following expected utility:

Image vthd

Timeline. At t = 0, the CCP chooses its own capital and the collateral requirement to maximize its expected utility. The sellers and buyers are randomly matched and they observe the CCP’s capital and the collateral requirement. If they decide to trade, they pay the CCP the clearing fee and the sellers deposit (1 + a)c with the CCP. At t = 1, the payoff of the risky asset is realized. If the bad state is realized, some of the sellers may default (depending on how high the collateral is). If so, the CCP will allocate the default losses in accordance with the default waterfall.

Image iqgm

Traders’ state-contingent payoffs and expected utilities. The default waterfall changes the state-contingent payoffs of the buyers and sellers. For the buyers, they are fully insured only if the prefunded resources can cover all default losses. In those cases, they will receive nQ in both states. Otherwise, they will receive nQ in the good state and nQ - w in the bad state.

The sellers all receive (1 - n)Q in the good state. In the bad state, if nobody defaults, seller j has negative payoff of -(1 - rj)nQ. If some sellers default, the sellers’ payoffs in the bad state vary across the defaulting sellers and the non-defaulting sellers.The defaulting sellers in the bad state have a negative payoff of -(1 + a)c. The payoffs of the non-defaulting sellers in the bad state depend on whether their default fund contributions will be used to cover the losses:

  • If the default fund is not used, the non-defaulting sellers have a negative payoff: -(1 - rj)nQ.
  • If the default fund is partly used, they have a negative payoff: -(1 - rj)nQ - d.
  • If the default fund is depleted, they have a negative payoff: -(1 - rj)nQ - ac.

Let b denote the state-contingent payoffs for the homogeneous buyers and sj denote the state- contingent payoffs for seller j. The buyers are risk-averse and have mean-variance utility. Let у denote the risk aversion of the buyers. The expected utility of a buyer is

Image 3thn

2. The parameter assumptions

In what follows, I focus on the relevant cases where collateral and capital matter. Hence, the fol­lowing assumptions are imposed. Assumption 1 specifies that the collateral cost is not negligible. If collateral cost is so low that every seller can provide full collateral, nobody will default when the bad state is realized. To exclude that scenario, it is necessary to establish some lower bound for the collateral cost.

The loss of the seller with zero loss-reduction capacity (r,- - 0) in the bad state is largest among the sellers: лв. If this seller would provide full collateral, the associated collateral cost is 5лв. I  assume that such cost is larger than the utility gain from the buyer’s risk aversion, which ensures that the utility improvement for this pair of traders is negative:

Image 77kx

It is only meaningful to talk about capital when the capital cost is not so large that it could be destructive for the total welfare surplus. If the capital cost is so large that holding capital itself is costly enough to cancel out the utility gain from trading, it is optimal for the CCP not to hold capital. To exclude such a scenario, assumption 2 establishes an upper bound for the capital cost.

When all sellers default in the bad state (i.e., collateral is zero), the amount of capital that would be needed to cover the losses reaches the maximum: 0(1 - rj)nQ dr,-. The cost of holding capital is p 0 (1 - rf)nQ dr,. Such a level of capital can ensure that the buyers are fully insured. The utility gain from the risk-averse buyers is Yn(1 - n)Q2. For the capital cost not to be welfare-destructive, the utility gain should outweigh the associated capital cost:

Image x2yj

This establishes the upper bound for the capital cost.

Image 1wcp

3. A for-profit CCP

In this section, I study the case of a for-profit CCP protected by limited liability. The CCP chooses capital K and collateral requirement c to maximize its expected utility UCCP as specified in equation 1. The key trade-off is between fee income and counterparty risk.

I solve the for-profit CCP’s optimal problem by backward deduction. I first study whether the buyers and sellers will trade or not when c and K are given. To achieve this, I show when the sellers will default, and how the default losses will affect the traders’ utility improvement as they eat up different layers of the default waterfall. This determines trading volume v as functions of c and K.

With the trading volume, I can derive the optimal collateral and capital for the CCP. To elabo­rate the underlying intuitions, I study the optimal collateral policy conditional on a given amount of capital. Then I solve the optimal capital and the associated optimal collateral.

3.1 Traders’ utility at different layers of the default waterfall

Collateralized financial resources. When a seller defaults, both his collateral c and default fund contribution ac will be used to cover his default loss. Hence, both the collateral and the default fund contributed by a defaulting seller are collateralized financial resources. Correspondingly, the default fund contributed by the non-defaulting sellers is mutualized financial resources.

Sellers default strategically. When the payment a seller needs to make exceeds his collateral­ized financial resources, he defaults. For this reason, seller j with loss-reduction capacity rj will not default if and only if

Image u8n7

Reorganizing the inequality above, with a given c, seller j with loss-reduction capacity higher than ne~((1g+a)c (= V) will not default in the bad state. Let’s call seller j with loss-reduction capacity r the “marginal seller”. The loss-reduction capacity Vj can be interpreted as seller j’s creditworthi­ness as a counterparty.

When seller j does not default, i.e., Vj > r, the buyer of seller j receives nQ in both states. Seller j receives (1 - n)Q in the good state and pays (1 - Vj)nQ in the bad state. To clear the trade, both parties need to pay the clearing fee 2 and seller j needs to bear the collateral cost. The utilities of trading for the buyer and seller are

Image 9ft9

Equation 8 shows the utility improvement from trading for a pair of traders. Even though all buyers face the same counterparty risk from their centrally cleared trades, a specific trade takes place if and only if the seller extracts positive utility from it. This is why the seller’s idiosyncratic loss reduction capacity enters in (8), and hence determines whether a trade takes place or not.

Image xao4

When seller j has a loss-reduction capacity lower than V, he defaults if the bad state is realized. In that case, both the payoff of the loss-reduction capacity vpQ and the collateralized financial resources (1 + a)c are seized by the CCP. The remaining loss is (1 - vj)nQ - (1 + a)c. Hence the default loss that needs to be borne by the CCP L is a function of c:

Image 94e3

According to the default waterfall, the default losses will be covered first by the collateralized financial resources and the CCP’s capital. From equation 9, the mutualized financial resources are

Image tt4r

In this case, as the remaining loss is covered by the CCP capital, the buyer’s payoffs remain the same. The costs due to clearing are the same. However, seller j only needs to pay (1 + a)c in the bad state due to the strategic default. Equation 11 shows the utility improvement for this pair of traders.

Image wnhd

The traders’ utility improvement from trading decreases in collateral c, as shown in equation 8 and 11. If the CCP sets a high collateral requirement, traders need to bear a high collateral cost. Moreover, for a seller who has a low loss-reduction capacity, the high collateral cost will drive the utility improvement of trading to zero (or negative). Hence, the trading volume is a decreasing function of collateral.

The trading volume, however, does not strictly decrease in collateral, given that defaulting sellers have a “floor” for their downside risk: the maximum they can lose is the collateralized resources. Figure 1 shows the relationship between the utility improvement and the loss-reduction capacity. There is a kink at Г. The kink means that the trading volume will jump to 1 when the collateral is below some threshold. Let r denote the loss-reduction capacity threshold above which a seller will trade and not default, i.e., AUND(r, c) = 0. This means r is a function of c. When r(c) = r(c), the trading volume will jump to 1 because of the kink. Thus, this determines a collateral threshold c below which the trading volume reaches the maximum. Lemma 1 formalizes the idea.

Image 96a2

Mutualized financial resources. When K < K(c), the mutualized financial resources are used to cover the remaining loss. As long as the mutualized resources are large enough, the buyers are fully insured. Hence, K should satisfy the following condition:

Image cx1b

where K is

Image 1akg

and f stands for the loss-reduction capacity threshold at which a seller will trade and not default (elaborated later in equation 15). Note that f is different from r: The utility improvement of a non-defaulting seller and his buyer is smaller in this case because of the expected loss from default fund contribution.

As specified in the default waterfall, the non-defaulting sellers share the remaining loss evenly. d is the default fund loss for each non-defaulting seller:

Image bi4q

Given the definition of f, the following condition holds: AUND,M(f) = 0, which makes it an implicit equation that pins down f. Moreover, since A UND,M is a function of both c and K, f is not only a function of c (as is f) but also a function of K.

Figure 2 shows the utility improvement when the mutualized financial resources are used. As in Figure 1, seller j with loss-reduction capacity lower than r are the defaulting sellers. Unlike in Figure 1, not all sellers with loss-reduction capacity higher than r will trade because of the expected loss from their default fund contribution. The sellers with loss-reduction capacity between fr and ff will not trade.

Image 5gkr

The utility improvement from trading indicates how the trading volume v varies given c and K. When c > C, only non-defaulting sellers will trade with their buyers. The trading volume is

  1. - r. When 0 < c < C, the trading volume is not always one because the non-defaulting sellers anticipate losses from their default fund contributions. In this case, the collateral affects the trading volume via two channels. First, the collateral cost reduces the utility improvement. The trading volume decreases as the collateral increases. Second, the higher the collateral requirement, the lower the remaining loss that would need to be covered by the mutualized resources. The trading volume increases along with the collateral requirement. When r(c, K) = r(c), the trading volume is one and that pins down the collateral that achieves the maximum trading volume: c(K). Lemma
  2. summarizes the trading volume when mutualized resources are used to cover the remaining loss.

Image kzkn

Image ehtu

Image l0rb

When c > c, the trading volume is 1 - r because only non-defaulting sellers will trade. When 0 < c < c, the trading volume could be affected by the collateral in the following ways. First, the collateral cost reduces utility improvement. Thus, trading volume decreases as the collateral
requirement increases. Second, the higher the collateral requirement, the larger the default fund losses that the non-defaulting sellers need to bear. So the trading volume of the non-defaulting sellers decreases in the collateral requirement as well. Third, the higher the collateral requirement, the lower the utility loss from partial insurance. The trading volume will be one when r(c) = r(c, K), which determines the collateral that achieves the maximum trading volume: c(K). Lemma 3 summarizes the results.

Image wc63

Image 4n3j

Four cases with different combinations of c and K. The default waterfall specifies the sequence in which resources contributed by the CCP and the traders are used, which gives rise to the inter­twined incentives between the CCP and the traders. Panel A of Table 1 presents the traders’ utility improvement from trading when the default losses eat up different layers of the default waterfall. It shows how the utility improvement depends on the sellers’ heterogeneous loss-reduction capacity (rf) and the choice variables of the CCP (c, K). Based on the utility improvement, Panel B shows the thresholds that separate traders who will trade and those who will not. Because only the traders with positive utility improvement will trade.

Image 5cim

With all these elements in place, one could have four cases with different combinations between c and K, as shown in Figure 4. Given a pair of (c, K) at t = 0, both the CCP and the traders can “foresee” what would happen at t = 1 if the bad state is realized. Depending on whether the traders have a positive utility improvement, they will decide to trade or not to trade, which in turn determines the volume-based fee income of the CCP.

3.2 Optimal collateral and capital for a for-profit CCP

The expected utility of the CCP depends on c and K. When c > C, there is no default loss for the CCP at t = 1. Hence, the expected value of the CCP only consists of the volume-based fee income and the cost of capital. When 0 < c < c, UCCp takes two different expressions, depending on how large the CCP capital is. When K > K(c), defaulting sellers and their counterparties would like to trade. The CCP will cover the total default loss, i.e., (ne~(21?+)a)c^, at t = 1 if the bad state is realized. When 0 < K < K(c), the CCP contributes only its capital but does not cover all default losses when the bad state is realized.

Image hdi8

Optimal collateral policy when the CCP’s own capital is given. Although the CCP chooses the optimal collateral and capital simultaneously, I divide the decision procedure into two steps in order to facilitate the comparison between the CCP’s choice and the optimal collateral and capital in terms of maximizing social welfare, which will be discussed in Section 4.

There are several important observations from equation 21. First, when K > K(c), the CCP trades off between high fee income and high counterparty risk. On the one hand, the CCP could set collateral higher than c to minimize the counterparty risk. However, the volume-based fee income will be low. On the other hand, the CCP could set collateral lower than c to maximize the trading
volume, hence maximizing the fee income. But the default losses will be high.

The optimal collateral depends on which leads to a higher expected value of CCP. As a result, the fee level is a crucial element in determining the optimal collateral. There exists some threshold f where fv(c, K) = fv(c, K) -(1-л). Intuitively, when the fee level is low, i.e., f < f, the temptation for the CCP to increase the trading volume is small because the sensitivity of the CCP’s expected utility to the trading volume is low. The CCP cares more about the expected default losses and will set a high collateral. However, when the fee level is high, i.e. f > f the CCP has a strong incentive to maximize the trading volume and will go for a low collateral.

Second, when 0 < K < K(c), there is no trade-off (in setting collateral) between large trading volume and large default losses, as the CCP is protected by the limited liability and does not cover all the default losses. As K decreases, the CCP tends to chase high trading volume since it has very little to lose. Thus, when K is smaller than some threshold K where fv(c, K) = f - (1 - п)К, the CCP will set collateral c to reach the maximum trading volume.

Image da6d

Optimal capital for a for-profit CCP. With the optimal collateral policy when the capital is given, one could have the CCP’s expected utility as functions of the capital. When f < f, the CCP’s expected utility is

Image xaoe

Image p60d

As the CCP’s expected utility is a decreasing function of K, the minimum capital leads to the highest expected utility for the CCP.

In this section, I solve the optimal capital and collateral for a for-profit CCP with limited li­ability. The current design of the default waterfall, coupled with the central clearing mandate, creates intertwined incentives between the CCP and the traders. Since it is the traders who decide whether they would like to trade (and clear) the contract, they are effectively the risk takers. How­ever, their risk-taking behavior is constrained by the CCP’s collateral policy. Hence, the CCP is a gate-keeper that sets the risk management standards. Nonetheless, the default waterfall of the for-profit CCP puts the traders in the position of residual risk-bearers, since the remaining loss that exceeds the CCP’s capital will be covered by the surviving traders. This separates the (risk management) decision-making and the residual risk-bearing. As a result, the optimal capital and collateral chosen by a for-profit CCP are not the socially optimal ones.

 4. Optimal capital regulation for a for-profit CCP

In the previous section, there is no capital regulation for a for-profit CCP. The CCP chooses the amount of capital and collateral that will maximize its own expected utility. In this section, I introduce a regulator who maximizes the total welfare surplus by setting the capital for the CCP:

Image jxu9

Note that the optimal capital regulation will take the CCP’s optimal collateral policy as given, which is written in Proposition 1. Proposition 3 summarizes the optimal capital requirements for a for-profit CCP. The optimal capital requirement depends on the fee level f. When f > f, the clearing business is so profitable that the fee income of an additional trading volume is larger than the potential losses of that additional volume. The for-profit CCP will always chase a high trading volume to maximize its profits. In this case, a high level of capital does not help to reduce default losses in the bad state. Instead, a high capital requirement leads to a high collateral and makes clearing expensive for traders. The optimal capital in this case is to maintain a safe CCP with the least collateral possible. Hence, K* = K(c). When f < f, a high level of capital weights in the for-profit CCP’s optimal collateral policy. When the total welfare surplus with no default is larger than that with defaults, K* = K(c) leads to a high level of collateral C which disincentivizes sellers’ defaults.

Image vh7t

Although clearing fees in general are not policy instruments, they could be indicative policy variables for regulators. Clearing fees vary a lot. For the standard plan in LCH SwapClear,the clearing fees for interest rate derivatives are $0.9-$18 per million for a new trade with maturity ranging from overnight to 50 years. For the standard plan in LCH EquityClear, however, the fees for cash equities are less than $0.003 per million. Thus, the temptation for a for-profit CCP to lower its collateral requirement and increase trading volumes varies with different levels of clearing fees. Hence, clearing fees could be informative policy variables for regulators when setting optimal capital requirements for for-profit CCPs.

5. Comparison with a user-owned CCP

In this section, I analyze the optimal collateral and capital for a user-owned CCP. Some CCPs are indeed owned by their clearing members. These include, for example, Japanese Security Clearing Corporations (JSCC) and Swiss SIX X-clear Ltd. When a CCP is owned by the users, i.e., the buyers and sellers, it will maximize the total welfare surplus W, including the utility improvement of the buyers and sellers and the CCP's expected utility, by setting the optimal collateral and capital:

Image 0s2y

Similar to the analysis of a for-profit CCP, the traders’ decisions depend on the relationship between collateral requirement (c) and the CCP’s capital (K), as shown in Figure 4. However, the different objective function in the case of a user-owned CCP leads to different optimal collateral and capital policies.

When no seller defaults (c > C), the total welfare surplus decreases in both collateral and capital because of the collateral and capital costs. The optimal collateral and capital would be the lower bound. Lemma 4 summarizes the optimal capital and collateral in this case. Compared with the case with the for-profit CCP, this total welfare surplus is larger because there is no capital cost.

Image avis

When 0 < c < c, K > K(c), the sellers’ default losses would be covered by prefunded resources. The expected gains from defaults for the defaulting sellers are subsidized by the user-owned CCP’s capital and the default fund contributed by the non-defaulting sellers. Since capital is costly, the constraint that K > KT(c) should be binding. One can substitute K with KT(c) and maximize the total welfare surplus with respect to c. Lemma 5 shows the optimal capital and collateral in this case. It turns out that the optimal collateral is zero when the capital cost is smaller than the collateral cost. In order to cover the default losses, the user-owned CCP needs to hold a capital of у. When the capital cost is larger than the collateral cost, it is better to charge some collateral so that less capital is needed to cover default losses.

Image cxvt

When 0 < c < c, 0 < K < KT(c), the total welfare surplus is always lower than in the previous case because of the utility loss from the partial insurance. Hence comparing the total welfare surplus in the first two cases leads to the optimal collateral and capital policy for a user-owned CCP. Proposition 4 summarizes the optimal capital and collateral for a user-owned CCP.

Image 8f9o

This section studies the optimal capital and collateral for a user-owned CCP with costly col­lateral and capital. The user-owned CCP, unlike the for-profit CCP, always maintains sufficient prefunded resources. Hence, there is no dead-weight loss associated with the user-owned CCP.

6. Empirical results

According to the model, there are following testable hypotheses:

Image sjzc

The quantitative disclosure data from CCPs enable some empirical tests of these hypothe­ses. CCPs have been required to disclose quantitative information related to the PFMI since the second half of 2015 (CPMI-IOSCO, 2015). Following the CPMI-IOSCO disclosure framework, information is available on a CCP's skin-in-the-game and its clearing members' total default fund contribution (item 4.1), and the total required initial margin at each quarter-end (item 6.1). The CCP quantitative disclosure data are from the CCPView of Clarus Financial Technology. On top of that, I collect CCP ownership information from public sources. Appendix C shows the list of CCPs and their ownership structure. In total, there are 16 CCPs at the group level and 44 CCPs at the entity level, covering the majority of the clearing industry. The data are at quarterly frequency and range from 2015 Q3 to 2017 Q4.

It is noteworthy that, for CCP skin-in-the-game, there are different concepts in the quantitative disclosure framework:

  • Item 4.1.1 Prefunded - Own Capital Before (Default Fund)
  • Item 4.1.2 Prefunded - Own Capital Alongside (Default Fund)
  • Item 4.1.3 Prefunded - Own Capital After (Default Fund)
  • Item 4.1.7 Committed - Own/parent funds that are committed to address a participant default

For the theoretical model, item 4.1.1 is what I defined as CCP skin-in-the-game, which is the CCP’s resources that would be used before the surviving members’ default fund contributions. To serve as robustness checks, I also run regressions based on the other items. Let SrTOpfore denote item 4.1.1; SIT&ff + l .. denote the sum of item 4.1.1 and 4.1.2; SIT&ff + l d + denote

Image t1n5

the sum of item 4.1.1, 4.1.2 and 4.1.3; siTOcommMed denote the sum of all the four items.

This figure plots total initial margin against CCP skin-in-the-game (SITGfore) for each CCP. The red stars stand for for-profit CCPs and the blue circles are user-owned CCPs.

Image f4pc

6.1 Impact of CCP skin-in-the-game on required collateral

To test the first hypothesis, I utilize the panel data for the for-profit CCPs. Let i denote CCPs, j denote jurisdictions and t denote quarters. For CCP i in jurisdiction j at quarter t, IMi, j,t is the total initial margin required by the CCP and SITGi,j,t is the CCP’s skin-in-the-game.

One potential factor that drives the heterogeneity of CCP capital and margin is that CCPs in different jurisdictions may be subject to different levels of regulatory requirement and supervision. Hence, “better” CCPs have higher capital and higher collateral requirements because of “better” supervision, which has nothing to do with their incentives. To control for such time-varying juris­dictional differences, I include Jurisdiction x Time fixed effect j. Moreover, as different CCPs (at the entity level) clear different products, there could be product-specific or entity-specific features. To capture these features, I include a CCP fixed effect ai. To account for the scale differences, I take the natural logarithm of IMit and SITGit. The panel regression model is

Image 6o5q

6.2 Impact of ownership structure on CCP’s skin-in-the-game

To test the second hypothesis, I utilize the cross-sectional data for both the for-profit CCPs and the user-owned CCPs at the end of 2017. Let D'°r~profit denote the dummy variable for CCP i’s ownership structure: 1 stands for for-profit and 0 stands for user-owned. CVt is the control variable. The cross-sectional regression model is

Image jgsl

I use the size of initial margin and the default fund contributions as proxies for the size of a CCP. Moreover, I also calculate the ratio between a CCP’s skin-in-the-game to the financial resources contributed by clearing members, and see if this ratio varies across ownership structure.

Image qrhg

7. Conclusion

Following the post-crisis regulatory reforms, standardized OTC derivatives are now centrally cleared. This puts CCPs in the spotlight, as potential pressure points in the financial system. At the same time, many CCPs are for-profit public companies with limited liability and face a trade-off between fee income and counterparty risk. The theoretical model presented in this paper shows that, when there is no capital requirement, a for-profit CCP chooses to hold no capital and to set a low collat­eral requirements for its clearing members. By contrast, a user-owned CCP chooses to hold a high level of capital. This is supported by the empirical evidence.

The current static model does not take into account the disciplinary effect of the franchise value. Franchise value, which is defined as the present value of the future profits that a firm is expected to earn as a going concern, can indeed play the role of skin-in-the-game. One could argue that, in a dynamic setup, a for-profit CCP will charge a prudent level of collateral to protect itself from insolvency even with the minimum capital it chooses to hold. However, franchise value does not always provide self-disciplinary incentives when excessive risk goes hand-in-hand with franchise­enhancing expansions (see, e.g., Hughes et al., 1996). One example for a for-profit CCP is whether or not it should provide clearing services for a new product that will increase its franchise value and the overall riskiness of the clearing system, such as Bitcoin futures. The static model could serve as the first step. It would be an interesting avenue for future research to extend the model, with a view to investigating under what conditions the franchise value of CCPs could safeguard them from failure.

Image dsfv

Image iys0

Image s1sk

Image 32iu

Image bijc

Image 2aou

Image 8hhr

Image xzr

Image e4wf

Image ozvr

Image fp0m

Image p0ig

Image nf89

Image 3xqb

Image 3xqb

Image gl04

Image vzfk


Acharya, Viral and Alberto Bisin. 2009. “Centralized versus Over The Counter Markets.” New York University, Working Paper :2007-2008.

-- . 2014. “Counterparty risk externality: Centralized versus over-the-counter markets.” Jour­nal of Economic Theory 149:153-182.

BCBS-CPMI-IOSCO-FSB. 2017. “Analysis of Central Clearing Interdependencies.” Report, Bank for International Settlements.

-- . 2018. “Analysis of Central Clearing Interdependencies.” Report, Bank for InternationalSettlements.

Biais, Bruno, Florian Heider, and Marie Hoerova. 2012. “Clearing, counterparty risk, and aggre­gate risk.” IMF Economic Review 60 (2):193-222.

-- . 2016. “Risk-sharing or Risk-taking? Counterparty Risk, Incentives and Margins.” Journal

of Finance 71 (4):1669-1698.

Bignon, Vincent and Guillaume Vuillemey. 2018. “The failure of a clearinghouse: Empirical evidence.” Review of Finance forthcoming.

BIS. 2018. “OTC derivatives statistics.” Available at https://stats.bis.org/statx/srs/table/d7 .

Boissel, Charles, Francois Derrien, Evren Ors, and David Thesmar. 2017. “Systemic risk in clearing houses: Evidence from the European repo market.” Journal of Financial Economics 125 (3):511-536.

Buding, Edwin, Robert Cox, and David Murphy. 2016. “Central counterparties in crisis: Inter­national Commodities Clearing House, New Zealand Futures and Options Exchange and the Stephen Francis Affair.” Journal of Financial Market Infrastructures 4 (3):65-92.

Capponi, Agostino, Jessie Jiaxu Wang, and Hongzhong Zhang. 2018. “Designing Clearinghouse Default Funds.” .

Carter, Louise and Megan Garner. 2016. “Skin in the game: central counterparty risk controls and incentives.” Journal of Financial Market Infrastructures 4 (3):39-54.

Carter, Louise, Jennifer Hancock, and Mark Manning. 2016. “Ownership, Incentives and Reg­ulation of CCP Risks.” In Analyzing the Economics of Financial Market Infrastructures. IGI Global, 272-303.

Cont, Rama and Thomas Kokholm. 2014. “Central clearing of OTC derivatives: bilateral vs mul­tilateral netting.” Statistics & Risk Modeling 31 (1):3-22.

Cox, Robert and Robert Steigerwald. 2016. ““Incomplete demutualization” and financial mar­ket infrastructure: central counterparty ownership and governance after the crisis of 2008-9.” Journal of Financial Market Infrastructures 4 (3):25-38.

Cox, Robert T and Robert S Steigerwald. 2017. “A CCP is a CCP is a CCP.” .

CPMI-IOSCO. 2012. “Principles for Financial Market Infrastuctures.” Report, CPMI-IOSCO.

-- . 2014. “Recovery of financial market infrastructures.” Report, CPMI-IOSCO.

-- . 2015. “Public quantitative disclosure standards for central counterparties.” Report, CPMI-


Cumming, Fergus and Joseph Noss. 2013. “Assessing the adequacy of CCPs default resources.” Bank ofEngland Financial Stability Paper (26).

Duffie, Darrell. 2015. “Resolution of Failing Central Counterparties.” .

Duffie, Darrell, Martin Scheicher, and Guillaume Vuillemey. 2015. “Central clearing and collateral demand.” Journal of Financial Economics 116 (2):237-256.

Duffie, Darrell and Haoxiang Zhu. 2011. “Does a central clearing counterparty reduce counterparty risk?” The Review of Asset Pricing Studies 1 (1):74-95.

Elliott, David. 2013. “Central counterparty loss-allocation rules.” Bank of England Financial Stability Paper (20):16.

Financial Times. 2014. “Banks launch clearing review after Korean broker default.” Available at https://www.ft.com/content/14b59838-a4d6-11e3-9313-00144feab7de .

Giancarlo, Christopher and Bruce Tuckman. 2018. “Swaps Regulation Version 2.0: An Assess­ment of the Current Implementation of Reform and Proposals for Next Steps.” White paper, Commodity Futures Trading Commission.

Hills, Bob, David Rule, Sarah Parkinson, and Chris Young. 1999. “Central Counterparty Clearing Houses and Financial Stability.” Manuscript, Bank of England Financial Stability Review 6: 122-134.

Hughes, Joseph P, William Lang, Loretta J Mester, and Choon-Geol Moon. 1996. “Efficient bank­ing under interstate branching.” Journal of Money, Credit and Banking 28 (4):1045-1071.

Koeppl, Thorsten, Cyril Monnet, and Ted Temzelides. 2012. “Optimal clearing arrangements for financial trades.” Journal of Financial Economics 103 (1):189-203.

Koeppl, Thorsten V. 2013. “The limits of central counterparty clearing: Collusive moral hazard and market liquidity.” Tech. rep., Queen’s Economics Department Working Paper.

Koeppl, Thorsten V and Cyril Monnet. 2010. “Emergence and future of central counterparties.” .