Staff Working Paper No. 580 Centralized trading, transparency and interest rate swap market liquidity: evidence from the implementation of the Dodd-Frank Act Evangelos Benos, Richard Payne and Michalis Vasios January 2016

Staff Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. Any views expressed are solely those of the author(s) and so cannot be taken to represent those of the Bank of England or to state Bank of England policy. This paper should therefore not be reported as representing the views of the Bank of England or members of the Monetary Policy Committee, Financial Policy Committee or Prudential Regulation Authority Board.

Staff Working Paper No. 580 Centralized trading, transparency and interest rate swap market liquidity: evidence from the implementation of the Dodd-Frank Act Evangelos Benos,(1) Richard Payne(2) and Michalis Vasios(3) Abstract

We use transactional data from the USD and EUR segments of the plain vanilla interest rate swap market to assess the impact of the Dodd-Frank mandate that US persons must trade certain swap contracts on Swap Execution Facilities (SEFs). We find that, as a result of SEF trading, activity increases and liquidity improves across the swap market, with the improvement being largest for USD mandated contracts which are most affected by the mandate. The associated reduction in execution costs is economically significant. For example, execution costs in USD mandated contracts, where SEF penetration is highest, drop, for market end-users alone, by $3 million–$4 million daily relative to EUR mandated contracts and in total by about $7 million–$13 million daily. We also find that inter-dealer activity drops concurrently with the improvement in liquidity suggesting that execution costs may have fallen because dealer intermediation chains became shorter. Finally, we document that the Dodd-Frank mandate caused the activity of the EUR segment of the market to geographically fragment. However, this does not appear to have compromised liquidity. Overall, our results suggest that the improvements in transparency brought about by the Dodd-Frank trading mandate have substantially improved interest rate swap market liquidity. Key words: Swap Execution Facilities, transparency, market liquidity.

JEL classification: G10, G12, G14.

(1) Bank of England. Email: [email protected] (2) Cass Business School. Email: [email protected] (3) Bank of England. Email: [email protected]

The views expressed in this paper are those of the authors and not necessarily those of the Bank of England or any of its policy committees. Please address comments to the authors via email. We are grateful to David Bailey, Paul Bedford, Pedro Gurrola-Perez, Rhiannon Sowerbutts and seminar participants at the Bank of England and the 2015 Workshop on Market Liquidity organized by the National Bank of Belgium, for helpful comments and suggestions.

Information on the Bank’s working paper series can be found at www.bankofengland.co.uk/research/Pages/workingpapers/default.aspx

Publications Team, Bank of England, Threadneedle Street, London, EC2R 8AH Telephone +44 (0)20 7601 4030 Fax +44 (0)20 7601 3298 email [email protected] © Bank of England 2016 ISSN 1749-9135 (on-line)

1

Introduction

This paper presents a study of how recent regulatory changes to OTC derivative markets have altered the quality of those markets. In particular, we study how implementation of the trade mandate of the Dodd-Frank act has impacted liquidity and trading patterns in interest rate swap markets. A key change to interest rate swap (hereafter ‘swap’) trading as a result of Dodd-Frank was the introduction of Swap Execution Facilities (SEFs).1 These are multi-lateral trading venues, featuring a multi-dealer request for quote (RFQ) functionality as well as an open limit order book (LOB). Since early October 2013, trading on SEFs has been possible for swap contracts on a voluntary basis and since February 2014, all trades in eligible swap contracts that involve US persons must take place on a SEF. This change has meant that swap trading has become more centralized and more pre-trade transparent, which is expected to have strengthened quote competition among dealers and increased the chances that ultimate counterparties trade with each other with less (or no) dealer intermediation.2 Our goal is to study whether this regulatory innovation has improved (or damaged) liquidity in swap markets. Understanding the impact of this aspect of Dodd-Frank is of profound importance not only because swaps constitute the world’s largest financial market (BIS (2014)) but also because the European Union is set to implement a similar set of rules by 2017 as part of its Markets in Financial Instruments Regulation (MiFIR). Prior to the implementation of Dodd-Frank, swap trading was largely decentralized and opaque. There was no central source for trade information and no liquidity hub that published pre-trade information on quotes and sizes. Therefore, buyers and sellers bore pecuniary and time costs when searching for quotes and counterparties (see Duffie et al. (2005) and Duffie (2012)). The opaque nature of the market and imperfect competition 1 Dodd-Frank also mandated central clearing of swap trades as well as post-trade disclosure of swap trade details. The clearing mandate came into effect on March 11, 2013 and the trade reporting mandate came into effect on December 31, 2012. 2 Pre-trade transparency in SEFs is achieved not only by the operation of the LOB, but also by the requirement that any RFQ must be disseminated simultaneously and instantly to multiple dealers. According to data from the National Futures Association (NFA), in practice, there is limited usage of the SEF LOB functionality. Hence, any increase in pre-trade transparency is mainly driven by trading through the RFQ mechanism. The trading mandate also aimed to increase post-trade transparency by requiring SEFs to publish daily trading activity on their websites. We describe SEF characteristics in detail in Section 2.

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among swap dealers may also have allowed the largest swap dealers to exploit other traders (see Kyle (1985) and Vayanos and Wang (2012)). These effects were likely to discourage agents from participating in these markets and to reduce competition in liquidity supply. Thus we would expect that introducing SEFs to the trading landscape might improve participation and competition and increase liquidity.3 For our analysis we use proprietary data from the London Clearing House (LCH), supplemented with public data from the Depository Trust & Clearing Corporation (DTCC). Both sources contain information on executed swap transactions. The LCH data runs from the beginning of 2013 to the end of September 2014 and, in addition to the usual order book trade variables, contains counterparty information from which we can infer traders’ geographical locations (e.g. US versus non-US) and trader type (e.g. dealer or client). The data also tells us whether a trade was executed on a SEF. Before conducting any in-depth analysis, we look at some summary statistics and note that SEF trading penetrated the USD swap market to a much greater extent than it did the EUR swap market. This is likely due to the requirement to trade on a SEF if one is a US person and the smaller probability of US persons wishing to trade EUR denominated swaps. We also show that the introduction of the SEF trading mandate reduced the proportion of trading taking place between US and non-US persons, particularly for EUR denominated swaps. This suggests that some non-US persons became less willing to trade with US persons as this would require them to trade on a SEF. Thus, an effect of the new regulation was increased geographical fragmentation of the global swap market. We then employ a difference-in-differences technique to isolate the effects of the introduction of SEF trading on liquidity. Liquidity here is measured using dispersion of execution prices around a benchmark, as in Jankowitsch et al. (2011), or by Amihud’s liquidity measure (Amihud (2002)). The treatment group of assets in our difference-indifferences tests is the set of USD swaps that were required to trade on a SEF after February 2014. Our control groups are either the USD swaps that were not captured by the mandate or the EUR mandated swaps which are mostly traded by non-US persons 3

Theory predictions in Pagano and Roell (1996) also support the view that more transparency enhances liquidity. Evidence that links transparency with liquidity can also be found in Naik et al. (1999), Boehmer et al. (2005), and Flood et al. (1999). Foucault et al. (2010) offer a survey of the theoretical and empirical literature on market transparency.

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who, in turn, are not captured by the mandate. We show that the introduction of SEF trading was associated with a significant improvement in liquidity particularly for those USD denominated swaps that were mandated to trade on SEFs. We find that the effect is economically very significant with total execution costs decreasing, for USD mandated swaps, by as much as $20 - $40 million daily and by $7 - $13 million daily exclusively for market end-users (i.e. non-dealers) . We proceed to show that liquidity improvements in our sample are greatest for those swaps where SEFs are most heavily used. For example, relative to EUR mandated swaps, execution costs for USD mandated swaps drop by about $10 - $13 million daily ($3 $4 million daily for end-users). Thus, greater penetration of multi-lateral transparent venues improves trading conditions. We then explore if the observed improvements in liquidity are associated with changes in trading patterns among market participants. In particular, we examine if mandated and USD denominated contracts experienced a larger reduction in the amount of interdealer trading after the introduction of SEFs. The aim is to see if improved transparency reduced the importance of dealers in matching ultimate counterparties. In such a case, the shrinkage of the intermediation chain and the elimination of associated dealer markups may have been one of the reasons why execution costs decreased after the introduction of SEFs. We find that there is indeed a general reduction in inter-dealer trading following the introduction of SEFs. However, this reduction is almost equally spread across different maturities and currencies, which means that we can only make a weak causal claim between the mandate and the change in the trading ecology.4 Finally, we explore whether the observed geographical fragmentation of the global swap market has impaired market liquidity, a concern expressed in a number of industry reports (see for example ISDA (2014)). Broadly speaking, the fragmentation of liquidity provision could raise trading costs for investors through higher search costs for the best price, higher information asymmetries as well as limited competition among liquidity suppliers (see for example, Arnold et al. (1999) and Biais and Martinez (2004) among 4

This might be explained by the limited use of the SEF LOB (NFA data). If end-clients were trading directly with one another on a LOB, then we might see a more substantial change in trading patterns in mandated USD denominated contracts. On the other hand, RFQs are making dealers quote more competitive prices and so it is also plausible that we see narrower spreads with no change in the underlying trading ecology.

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others). Our analysis shows that although US and EU swap markets appear to have become less integrated as a result of Dodd-Frank, this has not had a detrimental effect on trading costs. A fragmentation measure, based on the proportion of trades conducted across versus within geographical regions, is unrelated to liquidity. Overall, our results show that the increased transparency and competition that SEFs brought about significantly improved trading conditions for swaps, especially those that were forced to trade upon them. This result that increased pre- and post-trade transparency improves liquidity chimes with those from work on other asset classes. For example, Boehmer et al. (2005) show that when the NYSE allowed traders, not located on the exchange floor, to see the contents of the limit order book, this resulted in a significant improvement in liquidity. Flood et al. (1999) study an experimental market and demonstrate that opacity, through its effect on search costs, reduces liquidity (although it causes price discovery to improve). Harris and Piwowar (2006) argue that the fact that smaller corporate bond trades are more costly to execute than large trades is due to large trades being done by large institutions with clear views of the market while small traders suffer a lack of transparency and thus greater costs. Goldstein et al. (2007), Edwards et al. (2007) and Bessembinder et al. (2006) show that introducing post-trade transparency to US corporate bond markets had, on balance, a positive effect on liquidity (exceptions were found for very thinly-traded bonds and for the largest trades). The work of Green et al. (2007) and Hendershott and Madhavan (2015), who also study corporate bond markets, is also related to ours. The first paper focuses on market power and its effects on trading. It shows how dealer market power increases execution costs and also demonstrates that market power is positively related to the length of intermediation chains. The second paper examines the efficacy of electronic venues at facilitating trading in OTC markets. The authors show that a periodic one-sided electronic auction mechanism can be a viable source of liquidity. Interestingly, this mechanism has some similarities to the SEF RFQ functionality in that it encourages dealer competition without disseminating trading intentions and dealer quotes to all market participants. This results in better prices while limiting information leakages. Finally, our work is related to that of Fulop and Lescourret (2015), Loon and Zhong (2014) and Loon and Zhong (2015). Fulop and Lescourret (2015) study the impact 4

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on liquidity of the contracts standardization (in 2009) and the reporting of aggregate weekly post-trade data (in 2008) in the single-name CDS market. They find that the standardization of CDS contracts improved liquidity across the market, while the posttrade data disclosure improved the liquidity only for a subset of CDS contracts. Loon and Zhong (2014) and Loon and Zhong (2015) also study the effects of Dodd-Frank, albeit they concentrate on the two other key provisions of the Act, namely centralized clearing and post-trade reporting. Employing data from the CDS market, they show that the introduction of a central counterparty to CDS trades reduces counterparty credit risk and, through its effect on post-trade transparency, improves liquidity. They also show that an increase in post-trade transparency brought about by post-trade reporting also contributed to improvements in liquidity. Our paper focuses instead on the impact of pre-trade transparency as related to the third pillar of the Dodd-Frank OTC derivatives regulation, namely the mandate for centralized trading. Our study also focuses on the IRS market which is substantially larger in terms of notional amounts outstanding. The rest of the paper is oraganized as follows. Section 2 sets out the regulatory changes that affected swap markets as a result of Dodd-Frank and gives a detailed description of SEFs. Section 3 describes our data sources and presents a set of summary statistics. Section 4 describes the variables we use to measure liquidity, our econometric models and their results. Section 5 concludes.

2 2.1

Policy Context and Institutional Details OTC derivatives and the Dodd-Frank Act

A major pillar of the US Wall Street Reform and Consumer Protection Act (the “DoddFrank Act”) concerns over-the-counter derivatives (OTCDs) markets. In particular, owing to concerns that insufficient collateralization and opacity in these markets contributed to widespread adverse selection and systemic risk during the crisis, Title VII of the Act implemented a series of reforms aimed at mitigating counterparty risk and improving pre- and post-trade transparency. As such, it mandates centralized clearing for eligible contracts, it requires real-time reporting and public dissemination of transactions and also requires that eligible contracts are to be traded on Swap Execution Facilities 5

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(SEFs), a form of multilateral trading venue. Because of its characteristics (described in detail in the next section), SEF trading brings about a step increase in the level of pre-trade transparency for the affected contracts. The Dodd-Frank trading mandate was implemented by the U.S. Commodity Futures Trading Commission (CFTC) in two phases. In the first, which took effect on October 2, 2013, SEF trading became available for OTCDs on a voluntary basis. This practically meant that the newly authorized trading venues had to comply with a number of principles and other requirements, including for instance the obligation to operate a limit order book and to automatically disseminate requests for quotes to multiple dealers.5 In the second phase, specific contracts were explicitly required to be executed on SEFs. The mandate captured a wide range of interest rate swap (IRS) contracts of various currencies and maturities as well as several credit default swap (CDS) indices. The determination of the mandated contracts was (and still is) primarily SEF-driven (through the Made Available to Trade (MAT) procedure). A SEF can submit a determination for a swap to CFTC, which then reviews the submission. Once a swap is approved or deemed certified as available to trade, all other SEFs that offer this swap for trading must do so in accordance with the requirements of the trade mandate. The criteria for MAT determination among others include the trading volume of the swap, whether there are available buyers and sellers as well as the frequency of transactions. Table 1 shows the mandated maturities along with the mandate date for the plain vanilla USD- and EUR-denominated IRS contracts which we use in our analysis. Most maturities were mandated on February 15 2014 with a couple more maturities following suit a few days later on the 26th. The SEF trading mandate only captures “US persons” with the definition of a US person being relatively broad.6 Importantly, the mandate affects the trades of US persons 5 This doesn’t mean that there were no electronic venues in operation or that no swaps were being traded on limit order books or other multilateral trading platforms before October 2, 2013. It only means that after this date, any venue that was officially recognized as a SEF had to comply with the specific CFTC minimum requirements mentioned above. Unfortunately, we have no data on the means of execution prior to October 2, 2103. Nevertheless, to the extent that swaps were already being traded on pre-trade transparent electronic platforms before this date should bias against finding any differences in market conditions when making a “before versus after” comparison. Our analysis shows that the differences were actually substantial. 6 Apart from US-registered swap dealers and major participants, the definition of a US person also includes foreign entities that carry guarantees from a US person (e.g. the foreign branch of a US

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regardless of who their counterparty is. In other words, if a US person is to trade a mandated contract with a non-US person, the trade has to be executed on a SEF. We next describe in detail the characteristics of SEFs and how exactly they improve pre-trade transparency.

2.2

Swap Execution Facility (SEF) Characteristics

Swap Execution Facilities (SEFs) are electronic trading platforms where, according to the CFTC, “multiple participants have the ability to execute swaps by accepting bids and offers made by multiple participants in the platform”. In practice, SEFs have two different functionalities to facilitate this. The first is a fully fledged central limit order book which allows any market participant to supply liquidity by posting bids and offers.7 Theoretically, this functionality allows end-users to bypass dealers altogether in concluding a trade, assuming of course that the order book has sufficient liquidity. The second functionality is a modification of the existing request-for-quote (RFQ) dealer-centric model. The innovation, relative to standard single-dealer platforms, is that a client’s request for a quote is disseminated simultaneously and instantly to multiple dealers instead of just one. This enables the client to easily compare prices across dealers and thus promotes competition for client order flow among dealers. The law required that a RFQ to be communicated to no less than two market participants during a phasein period until October 2014 and, subsequent to that period, to no less than three market participants. Upon transmission of the request for quote, the dealers may respond by posting their quotes to the client.8 Importantly, dealers cannot see each others’ quotes nor do they know how many and which other dealers have received the request. In dealer) and also any entities with personnel on US soil which is substantially involved in arranging, negotiating or executing a transaction. According to market reports this created initially some uncertainty as to who is captured. See for example:http://www.risk.net/risk-magazine/news/2256600/ broader-us-person-definition-could-cause-clearing-avalanche-participants-warn 7 For swaps that are subject to the trade mandate, SEF regulation also requires that broker-dealers, who have the ability to execute against a customer’s order or execute two customers against each other, be subject to a 15-second timing delay between the entry of the two orders on the LOB. This is intended to limit broker-dealer internalization of trades and to incentivize competition between market participants. 8 It is worth noting that CFTC did not impose any requirement that the identity of the RFQ requester be disclosed. This was due to concerns expressed by market participants that the disclosure of the RFQ requester identity would cause information leakages about future trading intentions. See Foucault et al. (2007) and Nolte et al. (2015) for a discussion on the implications of the disclosure of counterparty identities.

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addition, the market participants responding to the RFQ cannot be affiliated with the RFQ requester and may not be affiliated with each other. This arrangement makes it hard for dealers to collude and effectively renders the bidding process a first-price, sealed bid auction. The two trading functionalities are designed to operate in conjunction for swaps that are subject to the trade execution mandate (through SEF’s minimum trading functionality requirement). In practice, this means that a SEF must provide the RFQ requester with any firm resting bid or offer on the SEF’s order book together with any other quote received by the dealers from the RFQ platform. And the requester retains the discretion to execute either against the resting quotes on the LOB or against the dealers’ submitted quotes to the RFQ system.9 Upon execution of a transaction on a SEF’s LOB or RFQ system, the SEF can establish a short time period for a work-up session open to all market participants and in accordance with the minimum trading functionality. That is all market participants can trade an additional quantity of the same swap at the same price as the initial trade. The SEF’s trading protocol can provide the counterparties who initiated the first trade execution priority in the work-up session. Duffie and Zhu (2015) show that this type of work-up sessions can enhance price discovery and liquidity. Overall, SEFs change the micro-structure of the market in two important ways. First, they increase transparency in the IRS and CDS markets by allowing market participants to more easily compare prices quoted by dealers. Previously, if an end-user wanted to shop around for prices she would have to sequentially contact multiple dealers. This was both expensive and time consuming. Second, SEFs make it possible for the structure of the trading network to change by allowing end-users to compete directly with dealers in supplying liquidity. Thus, two end-users can in principle conclude a trade and completely bypass the dealers. In practice, most of the liquidity provision is still being done by dealers but as participation and search costs decrease there is potential for a larger fraction of volume to be diverted to the SEF limit order book. 9

Any trades of swap contracts that are not subject to the mandate can still be executed on a SEF and the SEF must offer an order book. However, the SEF is also free to offer any other method of execution (including bilateral trading and voice-based systems) for these trades.

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3

Data and Summary Statistics

3.1

Swap Transaction Data

In our analysis we use transaction data for USD and EUR denominated vanilla spot interest rate swaps, which we obtain from the LCH and the DTCC. LCH clears approximately 50% of the global interest rate swap market and more than 90% of the overall cleared interest rate swaps through the SwapClear clearing platform. Its services are used by almost 100 financial institutions from over 30 countries, including all major dealers. We obtain the reports of all new trades that were cleared by LCH between January 1, 2013 and September 15, 2014. The choice of this data implies that our analysis is not confounded by the other elements of Dodd-Frank. For instance, the reporting mandate became effective just prior to the beginning of our sample period whereas the contracts we use in our analysis were already being centrally cleared (by LCH), meaning that they were not affected by the clearing mandate. Each LCH report contains information on the date of trade, effective trade, maturity date, notional, swap rate, and other contract characteristics. In addition, a report includes the identities of the counterparties, which allows us to categorize trades by type of counterparty (dealer vs. non-dealer) and location (US, EU etc). Since April 2014 LCH reports also contain information on whether a transaction is executed on a trading venue, the name of the venue, as well as whether the venue is authorized as a SEF. We apply a number of filters to clean these data. First, we keep only spot starting swaps, which we do by removing any reports whose effective date is more than 2 business days from the trade date. Next, we remove duplicate reports. Duplicate reports exist because for every transaction that is centrally cleared, the clearing house produces one report per counterparty. We also remove any portfolio or compression trades as they are not price-forming.10 Finally, to remove any inaccurate or false reports we keep only trades where the percentage difference between the reported swap rate and Bloomberg’s end-of-day rate for the same currency and maturity is less than 5% in absolute value. 10 Compression trades are used in order to reduce the total notional amounts outstanding of the participating institutions, while leaving their net notional amounts unchanged. The purpose of this is to reduce the amount of counterparty risk (which is a function of gross notional) while maintaining the same level of exposure to market risk.

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Although LCH is the global leader in clearing interest rate swaps, there are other clearing houses that offer competitive services, for example the Chicago Mercantile Exchange (CME). To ensure that our results are representative of the whole clearing space, we complement the LCH data with data from the DTCC, a trade repository (TR) operator. As part of the Dodd-Frank Act, the CFTC required all US and certain types of non-US market participants to submit trade reports to swap trade repositories, which in turn make these data available to the public in real-time.11 The DTTC was the first to operate a TR on December 31, 2012, hence we extract all transactions that were reported to the DTCC between January 1, 2013 and September 15, 2014. DTCC reports contain information on many contract characteristics, including whether a trade is centrally cleared or executed on a SEF. Similar to the LCH data, we select centrally cleared USD and EUR denominated vanilla spot interest rate swaps. In addition, we remove any duplicates, cancelation reports, and any swaps with additional terms that affect the swap’s price. We also remove extreme prices and misreports by applying the same rules as used for the LCH data. The final step in our data cleaning methodology is the removal of any trades that were reported to both LCH and DTCC. To remove these duplicate reports we apply an algorithm that matches LCH and DTCC reports based on trade date, effective trade, maturity date, notional, swap rate, and other contract characteristics that are common in both data sets. After filtering the data, we are left with a sample of 223,111 trade reports which account for a total $58.6 trillion in traded notional over our sample period. In Figure 1 we show the time series of trading volume by currency. This figure illustrates the sheer size of the swap market with volumes hovering around $70-80 billion for each currency on a daily basis. We can also see that total volume is roughly equally split between USD and EUR denominated swaps. A unique feature of the LCH reports is that they contain information on the identities of the counterparties. Specifically, for every trade that is centrally cleared by LCH we can see the Business Identifier Code (BIC) code of the counterparties. BIC is a unique identification code for financial institutions approved by the International Organization 11

For more details see the CFTC’s “Interpretive Guidance and Policy Statement Regarding Compliance with Certain Swap Regulations” at http://www.cftc.gov/idc/groups/public/@newsroom/documents/ file/crossborder_factsheet_final.pdf.

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for Standardization (ISO). It has typically 8 characters made up of (i) 4 letters that identify the bank, (ii) 2 letters that identify the country, and (iii) 2 letters or digits that identify the city. The BIC allows us to identify the dealers in our sample. OTC derivatives dealers are primarily large international financial institutions that facilitate trading between end users. Following the standard practice in the literature we classify as dealers the top 16 banks in volume terms, while any other counterparty is classified as a client. In Figure 2 we present the shares of volume by type of counterparty. The majority of trades are between dealers, which is consistent with the commonly held view that a small number of dealers dominates the OTC swap market. Dealer-to-client trades account for about one-third of the market in both currencies. One difference between the two currencies is that the share of client-to-client trading activity for USD-denominated swaps is twice as large as that in EUR-denominated swaps. The BIC also allows us to classify trades by the location of the counterparties. We decompose trading activity into (i) trades between US financial institutions, (ii) trades between US and non-US financial institutions, and (iii) trades between non-US financial institutions. Figure 3 presents this decomposition. About 50% of trading in USDdenominated swaps involves a US and a non-US counterparty, 30% two US counterparties, and 20% two non-US counterparties. For EUR-denominated swaps, the US to non-US trading activity makes up only 14% of the sample, while the vast majority of trades, about 80%, are between non-US counterparties. To get more insight into the dynamics of SEF trading, we present the time series of on-SEF trading from January 1, 2013 to September 15, 2014 in Figure 4. We observe that after October 2, 2013, the date when SEF trading was introduced, the majority of USD-denominated swaps reported to DTCC are executed on swap execution facilities. The fraction of SEF trading for these swaps (blue line) increases steadily, from about 60% in October 2013 to over 80% in September 2014. On the other hand, SEF trading in EUR-denominated swaps is less pronounced: the fraction of SEF trading (red dotted line) hovers between 20% and 40%. Figure 4 demonstrates that the CFTC does not have the power to enforce the US trading mandate in markets that are dominated by non-US counterparties, for example 11

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the EUR-denominated swap market (see Figure 3). This observation motivates the empirical strategy employed later in the paper.

4

Methodology and Results

4.1

Liquidity Variables

To measure liquidity, we first use two price dispersion measures which proxy for execution costs. The first is a volume-weighted average of the relative differences between individual execution prices and the average execution price for contract i, on day t. It is defined as: DispV Wi,t

v u Ni,t uX V lmk,i,t  Pk,i,t − P¯i,t 2 =t V lmi,t P¯i,t

(1)

k=1

where Ni,t is the total number of trades executed for contract i on day t, Pk,i,t is the execution price of transaction k, P¯i,t is the average execution price on contract i and day P t, V lmk,i,t is the volume of transaction k and V lmi,t = k V lmk,i,t is the total volume for contract i on day t. The second is the dispersion measure proposed by Jankowitsch et al. (2011). This is defined as: DispJN Si,t

v u Ni,t uX V lmk,i,t  Pk,i,t − mi,t 2 =t V lmi,t mi,t

(2)

k=1

where mi,t is the end-of-day t mid-quote of contract i. The scaling of these dispersion metrics by either the average execution price or the end-of-day mid-quote renders them comparable across contracts of different currencies and maturities. The last liquidity variable we use is the Amihud (2002) price impact, defined for contract i on day t as: Amihudi,t

T −1 1 X |Ri,t−j | = T V lmi,t−j

(3)

j=0

where we take T = 40 and V lmi,t is the total volume traded for contract i on day t, expressed in $ trillion. All of these liquidity measures have been used before in the context of OTC derivatives markets, for example in Loon and Zhong (2014).

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4.2

Panel diff-in-diff specifications

To assess the impact of SEF trading on market liquidity and activity, we estimate two panel specifications that implement difference-in-differences tests. The idea is to see if the impact of SEF trading on a treatment group of IRS contracts causes their liquidity to diverge from that of a control group after our event dates. As event dates we take the 2nd of October 2013 when SEF trading became available (and trades could be executed on SEFs on a voluntary basis) and the CFTC mandate effective dates shown in Table 1. On these dates (which vary across contract maturities) it became mandatory for US persons to trade the specific maturities on SEFs. Table 2 summarizes the main variables used in the difference-in-differences empirical tests that follow. Test 1: USD vs. EUR mandated contracts For our first diff-in-diff test we use the mandated USD-denominated contracts as a treatment group and the mandated EUR-denominated contracts as a control group. The USD segment of the IRS market has a substantially higher proportion of U.S. participants who are captured by the CFTC mandate. The EUR contracts, however, may be mandated but they are mainly traded by non-US persons who are not required to trade on a SEF. Thus, if transparency improves liquidity, we would expect the liquidity of USD contracts to improve relative to that of EUR contracts.12 An advantage of using the mandated EUR-denominated contracts as a control group is that both the treatment and control groups have similar liquidity profiles, which implies that our results are not subject to selection bias. On the other hand, liquidity and activity in the EUR segment of the market might be driven by different fundamentals. We control for this possibility by including a number of currency specific variables in our specifications.13 12

Of course, to the extent that SEFs are also used by participants in EUR mandated contacts, even to a lower degree, we would expect their liquidity to improve too, albeit by a smaller amount. 13 As discussed in the introduction, an effect of the new regulation was to increase the geographical fragmentation mainly in the EUR segment of the market. If fragmentation is positively associated with illiquidity, then this might imply that the SEF trading mandate would have introduced a negative externality to EUR contracts (ie, our control group). We carefully test if this was the case in Section 4.5 where we find that the observed geographical fragmentation is unrelated to liquidity.

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We implement this test by estimating the following panel specification: (1)

(1)

(2)

(2)

Lit = α+β1 Datet +β2 Curri Datet +β3 Datet +β4 Curri Datet +γ 0 Xt +ui +it (4) where i denotes currency-maturities and t denotes days. Lit is a liquidity or market activity variable. These are the dispersion and Amihud variables defined in equations (1) to (3) whereas our activity variables include daily volume traded, the daily number of trades executed and the number of unique market participants active on a given day. (j)

Datet , j = 1, 2 are dummies for the two event dates, Curri is a currency dummy that takes the value 1 for USD contracts and 0 for EUR contracts and Xt is a vector of market-wide controls that includes stock market returns, stock index implied volatilities, overnight interest rate spreads in both markets and yield curve slopes. These variables are included so as to control for the differences in fundamentals between the USD and EUR market segments. This specification explicitly disentangles liquidity/activity in the two currency groups as well as any changes in liquidity after the two events. The coefficients β1 and β3 capture any effects that are common to both market segments and coefficients β2 and β4 capture incremental effects that are particular to the USD market segment. We estimate the model using currency and maturity fixed effects and cluster the standard errors by both maturity and currency. Table 3 shows the results of this estimation. The models are estimated with and without the control variables, although there is little difference in the key coefficients across those two specifications. A first result to note is that after SEF trading became available on 2 October 2013 (Date(1) dummy) there is an improvement in liquidity for both market segments as the significantly negative coefficients on Date(1) and the insignificant interaction terms indicate. On the contrary, following the SEF mandate there is a clear differential effect between the USD and EUR segments of the market with the USD contracts showing a significant further liquidity improvement relative to the EUR contracts. These effects are economically very significant. For example, the coefficients for the Curr × Date(1) and Curr × Date(2) interaction terms in the dispersion specifications suggest that the marginal reduction in execution costs of the USD mandated versus the

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EUR mandated contracts is in the order of 12% to 14% of previous dispersion levels. This reduction in execution costs amounts to roughly $10-$13 million daily for all market participants (i.e. including dealers) and to $3-$4 million daily for market end-users. The total effect for USD mandated contracts is yet bigger with a drop in execution costs by about 22% to 27% of previous dispersion levels, which amounts to roughly $20-$40 million daily for all market participants and to $7-$13 million daily for end-users.14 The effect on the EUR contracts is also substantial despite the fact that fewer participants are captured by the mandate. The reduction in execution costs is about 10% to 12% or $10-$28 million daily. Regarding the activity variables, the results suggest that there was a reduction in activity for EUR contracts and a respective increase in USD contracts mainly after SEF trading became available. It is interesting here that although activity in EUR mandated contracts declined, liquidity actually improved, presumably because the market became more transparent. We don’t observe any significant difference in trading activity between the USD and EUR contracts after the second event (February 2015). Coefficients on the control variables are largely insignificant; the only consistently signed and significant coefficient is that on the VIX, which indicates that execution costs and activity rise in more volatile times, consistent with microstructure theory. Test 2: USD mandated vs. USD non-mandated contracts For the second diff-in-diff test we concentrate exclusively on USD contracts and use the mandated maturities as a treatment group and non-mandated USD swaps as the control group.15 This test has the advantage of comparing contracts whose prices are driven by the same set of fundamentals. P4 ¯ We calculate the reduction in execution costs for all market participants as: i=1 βi × V lm × P × M aturity where βi are the estimated coefficients from model (5), V lm is the average daily volume of USD mandated contracts ($75 billion), P¯ is their average volume-weighted price (1.7%) and M aturity is their average volume-weighted maturity (7 years). In doing this calculation we are assuming a zero risk-free rate which is realistic for the time period that we consider. We multiply further with the average fraction of dealer-to-client volume (33%) to estimate the reduction in execution costs for the market end-users. 15 The mandated maturities are: 2Y, 3Y, 5Y, 7Y, 10Y, 12Y, 15Y, 20Y and 30Y. The non-mandated maturities are: 1Y, 8Y, 9Y, 11Y, 13Y, 14Y, 25Y and 35Y. 14

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We implement this test by estimating the following panel specification: (1)

(1)

(2)

(2)

Lit = α+β1 Datet +β2 M ATi Datet +β3 Datet +β4 M ATi Datet +γ 0 Xt +ui +it (5) where now i denotes maturities and t denotes days. The key right-hand side variables used are the same as above with the only difference being that we now have a dummy variable (M ATi ) indicating whether a given contract maturity has been mandated by the CFTC. Also, as we are only dealing with USD contracts in this estimation, we shrink the control variable set to remove data from European equity and fixed income markets. We estimate this model using maturity fixed effects. Table 4 shows the results of these estimations, again both with and without control variables. The results are similar to those we obtained in the previous analysis. In particular, there is clear evidence of liquidity improvements for both mandated and non-mandated contracts after SEF trading became available on 2 October 2013. The improvement in liquidity is partially reversed for non-mandated contracts after February 2014 but remains intact for mandated ones. Overall, liquidity improves for both mandated and non-mandated USD contracts with the improvement being significantly greater for mandated ones. Thus, it appears that the liquidity improvements in the mandated contracts spilled over - to some extent - to non-mandated contracts. This is likely because market participants also chose to trade non-mandated contracts on SEFs as soon as the functionality became available, and presumably also because more transparency for some quoted prices on the maturity curve gives market participants a better idea of what a fair quote is for other maturities. As far as activity is concerned, again, there is a positive effect only for the mandated contracts which materializes after 2 October 2013.

4.3

Liquidity and SEF trading

We next test directly how the fraction of SEF trading relates to our liquidity and market activity variables. For that, we utilize the DTCC segment of our data which contains a flag indicating whether a given trade was executed on a SEF or not. Figure 4 shows the fraction of volume for USD and EUR-denominated plain vanilla

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IRS contracts that is traded on SEF, as captured by the DTCC data. One can see that for the contracts of both currencies volumes become positive after SEFs become available on 2 October 2013. Additionally, USD-denominated contracts generally have a higher degree of SEF trading than EUR-denominated ones. This is likely because a larger fraction of participants in the USD segment of the market are US persons who are captured by the SEF mandate. To assess the impact of SEF trading on liquidity and market activity, we estimate the following panel specification for mandated USD and EUR-denominated contracts only, on a daily frequency: (1)

Lit = α + β1 SEFit + β2 Datet + γ 0 Xit + ui + it

(6)

In this setup, Lit is the liquidity or market activity variable of contract i on day t. As before, we use the dispersion metrics and the Amihud measure defined in equations (1), (2) and (3) in order to capture liquidity and also total volume, number of trades and number of unique participants to capture market activity. SEFit is the percentage of (1)

SEF trading, Datet

is a date dummy taking the value of 1 after the introduction of

SEFs on 2 October 2013 and Xit is the same vector of controls used previously. We include the date dummy in the specification so as to see if the time and cross-sectional variation in SEF trading, conditional on SEF trading being available, has incremental explanatory power. Because it is possible that SEF trading is itself caused by market liquidity, we also estimate this model by IV, instrumenting SEFit with its own lags. Table 5 shows the results of this estimation. The coefficients on the percentage of SEF trading are significant throughout and consistent with the previous findings. A higher fraction of SEF trading is associated with increased levels of liquidity as captured by reduced values for both the dispersion metrics as well as the Amihud variable. In particular, the coefficients in the dispersion specifications suggest that a one standard deviation increase in the fraction of SEF trading is associated with a reduction in execution costs of about 0.04% of the swap price. Given a combined average daily volume of about $150 billion for both USD and EUR-denominated contracts, this amounts to almost $7 million of daily savings.

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Similarly, SEF trading is positive and statistically significant in the case of the activity variables. A higher fraction of SEF trading is associated with higher volumes, more trades and a larger number of market participants. Overall, these results suggest that SEF trading is associated with robust and measurable improvements in market quality.

4.4

Liquidity and market structure

In this section we explore potential causes of the improvements in swap market liquidity that we have documented. The improvements in pre-trade transparency brought about by SEFs should in principle ease search frictions and increase price competition among dealers. With SEFs, end-users can easily compare dealer-submitted quotes and can choose to trade at the best available price. As such, a reduction in execution costs can materialize whether there is a change in the underlying trading patterns or not. On one hand, if all dealers are forced to offer more competitive quotes, a given enduser might still choose to trade with the same dealer as before the introduction of SEFs, albeit at better prices. On the other hand, the reduction in execution costs might be because of reduced intermediation between ultimate counterparties. Because of limited pre-trade transparency in OTC derivatives markets, an initial client-to-dealer trade may give rise to offsetting inter-dealer trades until an ultimate counterparty is found.16 This intermediation chain adds to the execution cost of the initial client trade as each dealer in the chain must be compensated. To the extent that the IRS market becomes more transparent through the introduction of SEFs, end-users should have opportunities to trade directly with multiple dealers and with each other (if they utilize the SEF order book functionality). In either case, this implies that SEFs may have reduced execution costs by reducing the length and cost of the intermediation chain. To test this hypothesis, we examine if the introduction of SEFs and the trade mandate have reduced the amount of inter-dealer trading. For this, we again employ differences16 See Shachar (2012) and Benos et al. (2013) for a discussion of the similarly structured CDS trading network.

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in-differences by estimating the following panel specifications: (1)

(1)

(2)

(2)

D2Dit = a + b1 Datet + b2 Curri Datet + b3 Datet + b4 Curri Datet

(7)

+ c0 Zt + vi + uit (1)

(1)

(2)

(2)

D2Dit = α + β1 Datet + β2 M ATi Datet + β3 Datet + β4 M ATi Datet + γ 0 Xt + υi + it

where D2D is the fraction of the inter-dealer trading volume over total volume. As before, i spans maturity-currencies and t denotes days. In the first specification the treatment group consists of all USD mandated contracts and the control group of all EUR mandated contracts. In the second specification, the treatment group consists of all USD mandated contracts and the control group of all USD non-mandated contracts. The idea is to see if inter-dealer trading has changed more for contracts that are more affected by the trading mandate. The control variables are similar to those employed before in the liquidity difference-in-differences estimations. Table 6 shows the results of these estimations. The first two columns correspond to the first specification and the next two to the second one. In each instance we report estimation results with and without the control variables. The results suggest that following the introduction of SEFs in October 2013, as well as the implementation of the mandate in February 2014, inter-dealer activity generally dropped for both USD and EUR mandated contracts. For USD non-mandated contracts there was only a decline after February 2014. The interaction terms in all four specifications are insignificant suggesting that there was no strong differential effect between the treatment and control groups. Thus, although there is a decline in inter-dealer trading following the introduction of SEFs and the mandate implementation, we cannot definitively conclude that the decline is a result of the trading mandate. Our results imply that either the mandate led to an equal reduction in inter-dealer trading across contract classes or that the reduction in inter-dealer activity was independent of the mandate. In the latter case, the new RFQ functionality could simply be forcing dealers to quote more competitive prices and so it is plausible to see narrower spreads with no change in the underlying trading ecology.

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4.5

Liquidity and market fragmentation

One concern among market participants and regulators shortly after the SEF mandate took effect was that it might lead to market fragmentation (ISDA (2014)). Since the mandate to trade on a SEF only applied to US persons, it was conceivable that European counterparties who wished to avoid (for whatever reason) trading on a SEF, might do so by trading exclusively with other European counterparties. Indeed, some reports released after the implementation of the trade mandate suggested that the market was becoming fragmented and that this was causing market quality to deteriorate (e.g. Giancarlo (2015)). In this section, we exploit our knowledge of counterparty identities in the LCH data and investigate this issue in some detail. We first classify all the market participants in the LCH data as US or non-US-based and calculate the percentage of trading volume that is executed between US and non-US counterparties (US2nUS).17 Figure 5 plots this percentage for USD and EUR-denominated contracts. It is evident that whereas no substantial effect takes place in USD-denominated contracts after the introduction of SEF trading, there is a clear drop in the fraction of US-to-nUS volume in EUR-denominated swaps. We therefore confirm that the EUR segment of the swap market became substantially more fragmented following the introduction of SEF trading. We conjecture that the observed difference between the two market segments is because of the much smaller proportion of US market participants in the EUR-denominated segment of the market: if a non-US counterparty wants to trade with another non-US counterparty and avoid executing on a SEF, they can do so much more easily for a EUR-denominated contract than for a USD-denominated one. Motivated by this figure, we next examine what this geographic fragmentation in trading implies for market quality and activity in the EUR-denominated segment of the IRS market. For this reason, we estimate a panel specification similar to those estimated before: (1)

Lit = α + β1 f ragmit + β2 Datet + γ 0 Xit + ui + it ,

(8)

17

In practice, the majority of non-US activity is generated in Europe reflecting the fact that most non-US dealers are European entities.

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where Lit is a liquidity/activity variable of contract i on day t, f ragm is a measure of the degree of fragmentation defined as: f ragm ≡ 1 −

U S2EU V lm , T ot V lm

Date(1) is the dummy marking the introduction of SEF trading on 2 October 2013 and the controls are the same as before. Table 8 shows the results of this estimation. Two key conclusions emerge from this Table. First, and consistent with anecdotal evidence, a higher degree of fragmentation is indeed associated with a slowdown in market activity as captured by the raw volume, number of trades and number of participants active. Second, fragmentation does not appear to have affected market liquidity. A detrimental effect would require the estimated coefficient β1 to be positive and significant. It is important to note that some of the concerns about fragmentation were explicitly about market depth decreasing and the potential price impact of trades increasing. The insignificant coefficient β1 in the Amihud variable specification suggests that this concern is not backed up by the data.

5

Summary and Conclusion

One of the pillars of the G-20 reform agenda for OTC derivatives markets is the requirement to migrate trading activity to more centralized venues, which facilitate greater transparency. In response, and as part of Dodd-Frank, US regulators have mandated that US persons should trade certain interest rate swap contracts on swap execution facilities (SEFs). These venues greatly enhance transparency by disseminating requests for quotes to multiple dealers and by featuring an electronic order book which allows any market participant to compete with dealers for liquidity provision by posting quotes. Using transactional data from the IRS market we assess the impact of SEF introduction on market activity and liquidity as captured by estimates of the effective spread and the price impact of trades. Consistent with much of the theoretical and previous empirical microstructure literature, we find that the introduction of SEF trading is associated with a substantial reduction in execution costs. This is particularly true for the USD mandated contracts which are the most affected, given that they are primarily traded 21

Staff Working Paper No. 580 January 2016

by US persons who are captured by the trade mandate. For these contracts, we estimate that the reduction in execution costs amounts to as much as $20 - $40 million daily. Our results also suggest that part of the reason behind the reduction in execution costs may be that the intermediation chains - as captured by the amount of inter-dealer trading - have shrunk, thus reducing the respective dealer markups. Since the introduction of SEF trading, there have been concerns that owing to the asymmetric implementation of the mandate exclusively for US persons, the IRS market might fragment geographically. Our analysis confirms this for the EUR-denominated segment of the market where some non-US counterparties switched to trading with other non-US counterparties presumably to avoid being captured by the mandate. Nevertheless, our data also suggests that so far there has been no measurable negative impact on liquidity as a result of this trade fragmentation. Finally, given the beneficial effect of SEF trading on USD contracts and the global nature of OTC derivatives markets, our findings suggest that extending the scope of the trading mandate to cover other sufficiently liquid swap markets would be desirable.

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References Amihud, Y., 2002. Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets 5 (1), 31–56. Arnold, T., Hersch, P., Mulherin, J. H., Netter, J., 1999. Merging markets. The Journal of Finance 54 (3), 1083–1107. Benos, E., Wetherilt, A., Zikes, F., 2013. The structure and dynamics of the UK credit default swap market. Bank of England Financial Stability paper No 25. Bessembinder, H., Maxwell, W., Venkataraman, K., 2006. Market transparency, liquidity externalities, and institutional trading costs in corporate bonds. Journal of Financial Economics 82 (2), 251 – 288. Biais, B., Martinez, I., 2004. Price discovery across the Rhine. Review of Finance 8 (1), 49–74. BIS, 2014. OTC derivatives statistics at end–December 2014. Boehmer, E., Saar, G., Yu, L., 2005. Lifting the veil: An analysis of pre-trade transparency at the NYSE. The Journal of Finance 60 (2), 783–815. Duffie, D., 2012. Dark markets: Asset pricing and information transmission in over-thecounter markets. Princeton University Press. Duffie, D., Gˆarleanu, N., Pedersen, L. H., 2005. Over-the-counter markets. Econometrica 73 (6), 1815–1847. Duffie, D., Zhu, H., 2015. Size discovery. National Bureau of Economic Research Working Paper No. 21696. Edwards, A. K., Harris, L. E., Piwowar, M. S., 2007. Corporate bond market transaction costs and transparency. The Journal of Finance 62 (3), 1421–1451. Flood, M. D., Huisman, R., Koedijk, K. G., Mahieu, R. J., 1999. Quote disclosure and price discovery in multiple-dealer financial markets. Review of Financial Studies 12 (1), 37–59. Foucault, T., Moinas, S., Theissen, E., 2007. Does anonymity matter in electronic limit order markets? Review of Financial Studies 20 (5), 1707. Foucault, T., Pagano, M., R¨oell, A., 2010. Market transparency. Encyclopedia of Quantitative Finance. Fulop, A., Lescourret, L., 2015. Transparency regime initiatives and liquidity in the CDS market. Working Paper. Giancarlo, C., 2015. Pro-reform reconsideration of the CFTC swaps trading rules: Return to Dodd-Frank. White Paper.

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Goldstein, M. A., Hotchkiss, E. S., Sirri, E. R., 2007. Transparency and liquidity: A controlled experiment on corporate bonds. Review of Financial Studies 20 (2), 235– 273. Green, R. C., Hollifield, B., Schrhoff, N., 2007. Financial intermediation and the costs of trading in an opaque market. Review of Financial Studies 20 (2), 275–314. Harris, L. E., Piwowar, M. S., 2006. Secondary trading costs in the municipal bond market. The Journal of Finance 61 (3), 1361–1397. Hendershott, T., Madhavan, A., 2015. Click or call? Auction versus search in the overthe-counter market. The Journal of Finance 70 (1), 419–447. ISDA, 2014. Cross-border fragmentation of global OTC derivatives: An empirical analysis. Research Note. Jankowitsch, R., Nashikkar, A., Subrahmanyam, M. G., 2011. Price dispersion in OTC markets: A new measure of liquidity. Journal of Banking & Finance 35 (2), 343–357. Kyle, A., 1985. Continuous auctions and insider trading. Econometrica 53 (6), 1315– 1335. Loon, Y. C., Zhong, Z. K., 2014. The impact of central clearing on counterparty risk, liquidity, and trading: Evidence from the credit default swap market. Journal of Financial Economics 112 (1), 91 – 115. Loon, Y. C., Zhong, Z. K., 2015. Does Dodd-Frank affect OTC transaction costs and liquidity? Evidence from real-time CDS trade reports. forthcoming, Journal of Financial Economics. Naik, N. Y., Neuberger, A., Viswanathan, S., 1999. Trade disclosure regulation in markets with negotiated trades. Review of Financial Studies 12 (4), 873–900. Nolte, I., Payne, R., Vasios, M., 2015. Profiting from mimicking strategies in nonanonymous markets. Working Paper. Pagano, M., Roell, A., 1996. Transparency and liquidity: A comparison of auction and dealer markets with informed trading. The Journal of Finance 51 (2), 579–611. Shachar, O., 2012. Exposing the exposed: Intermediation capacity in the credit default swap market. Working Paper. Vayanos, D., Wang, J., 2012. Market liquidity–theory and empirical evidence. National Bureau of Economic Research Working Paper No. 18251.

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Figure 1: Total traded volume (in $ billion) by currency. In this figure we plot the total volume of EUR-denominated and USD-denominated plain vanilla swaps. The sample covers every spot vanilla interest rate swap which was either cleared by LCH or reported to DTCC between January 1, 2013 and September 15, 2014.

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Figure 2: Volume shares by type of counterparty: In this figure we decompose the total volume into dealer-to-dealer (d2d ), dealer-to-client (d2c), and client-to-client (c2c) trading. The inner circle presents the volumes of USD-denominated swaps, while the outer circle presents the volumes of EUR-denominated swaps. The sample covers every spot vanilla interest rate swap which was cleared by LCH between January 1, 2013 and September 15, 2014.

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Figure 3: Volume shares by location. In this figure we decompose the total volume into USto-US, US-to-non-US, and non-US-to-non-US trading. The inner circle presents the volumes of USD-denominated swaps, while the outer circle presents the volumes of EUR-denominated swaps. The sample covers every spot vanilla interest rate swap which was cleared by LCH between January 1, 2013 and September 15, 2014.

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Figure 4: Fraction of SEF trading. In this figure we present the percentage of SEF trading in USD- and EUR-denominated swaps. The sample covers every spot vanilla interest rate swap transaction reported to DTCC. The vertical line marks the introduction of SEFs (October 2, 2013). The time period is January 1, 2013 to September 15, 2014.

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Figure 5: Fraction of US-to-nUS trading. In this figure we present the percentage of US-tononUS trading in USD- and EUR-denominated swaps. The sample covers every spot vanilla interest rate swap transaction reported to LCH. The vertical line marks the introduction of SEFs (October 2, 2013). The time period is January 1, 2013 to September 15, 2014.

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Table 1: SEF trading mandate dates by currency and maturity for plain vanilla IRS contracts used in our study.

Currency

Maturity

Effective date

USD EUR USD EUR

2,3,5,7,10,12,15,20,30 2,3,5,7,10,12,15,20,30 4,6 4,6

15/02/2014 15/02/2014 26/02/2014 26/02/2014

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Table 2: Summary statistics of daily values of the key variables, by currency. The table shows statistics on trading volume (Vlm) measured in $ billions; daily number of trades (Ntrades); daily unique number of active counterparties (Nparties); the fraction of SEF (SEF ), dealer-to-dealer (D2D), and US to non-US (US-2-nonUS ) trading. It also shows statistics on the two dispersion measures and the Amihud price impact measure described in Section 4.1. The data consists of all LCH and DTCC reported transactions for USD- and EUR-denominated plain vanilla swaps. The time period is January 1, 2013 to September 15, 2014. USD

EUR

Mean

Sd

Min

Max

N

Mean

Sd

Min

Max

N

5.66 72.88 22.04

7.36 95.18 12.25

0.02 4 2

64.58 676 61

5559 5559 5740

4.44 39.82 19.68

4.49 45.36 8.59

0.06 4 2

44.90 346 49

5463 5463 5791

0.48 0.53 0.47

0.44 0.24 0.21

0 0 0

1 1 1

5820 5740 5740

0.20 0.60 0.12

0.32 0.21 0.16

0 0 0

1 1 0.96

5072 5791 5791

0.72 0.91 12.92

0.47 0.58 15.40

0 0.05 0.55

4.16 4.29 131.13

5559 5559 4813

0.67 1.16 9.47

0.46 0.82 7.47

0 0.07 0.98

3.67 4.60 46.60

5463 5463 4917

Activity variables

Vlm ($ billion) Ntrades Nparties Market structure

SEF (%) D2D (%) US-2-nonUS (%) Liquidity variables

Disp (vw)(%) Disp (JNS)(%) Amihud

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Within-R2 N

Constant

Slope EUR

Slope USD

O/N Spread EUR

O/N Spread USD

log RDAX

log RSP 500

VDAX

VIX

Curr × Date(2)

Date(2)

Curr × Date(1)

Date(1)

0.8362*** (91.65) 0.054 8821

-0.2121*** (-10.98) 0.0162 (0.50) 0.1061*** (4.40) -0.1345*** (-4.85)

Disp (vw) -0.2907*** (-8.50) 0.0125 (0.39) 0.0820*** (2.95) -0.1341*** (-4.78) 0.0259*** (5.51) -0.0075 (-1.52) -0.7057 (-0.72) 0.8911 (1.48) 0.0292 (0.20) 0.3972*** (6.06) -0.1404*** (-4.22) 0.0179 (0.36) 1.0589*** (8.55) 0.070 8740

Disp (vw)

1.2040*** (107.22) 0.040 8821

-0.3284*** (-12.44) 0.0711* (1.84) 0.2056*** (5.05) -0.2178*** (-4.94)

-0.4242*** (-9.70) 0.0623 (1.65) 0.1155** (2.46) -0.2127*** (-4.83) 0.0247*** (5.02) 0.0046 (1.03) -1.6996* (-1.78) -3.1542** (-2.59) 0.6246** (2.46) 0.6557*** (5.74) -0.1767*** (-3.59) -0.0036 (-0.05) 1.5129*** (9.68) 0.060 8740

Liquidity variables Disp (JNS) Disp (JNS)

9.2612*** (43.55) 0.115 7843

-2.0951*** (-5.86) 0.1214 (0.11) 2.2344*** (5.12) -2.0705* (-1.79)

Amihud -1.6761*** (-4.14) 0.1817 (0.16) 1.4314*** (3.22) -2.0875* (-1.82) 0.1335*** (3.05) -0.0409 (-0.93) 9.2875** (2.33) 2.7264 (0.78) 0.2119 (0.23) -1.3207** (-2.35) 0.2386 (0.40) -2.4992*** (-2.99) 11.5093*** (7.40) 0.142 7783

Amihud

5.6516*** (25.80) 0.042 8821

-0.3433** (-2.61) 2.4496*** (3.15) -0.7535** (-2.74) 0.3077 (0.77)

Vlm -0.8100*** (-4.14) 2.4203*** (3.09) -0.4165 (-1.21) 0.3078 (0.74) 0.0524** (2.37) 0.0479 (1.45) -0.7808 (-0.16) -6.0868 (-1.59) -0.7333 (-0.65) 2.0782*** (3.65) -0.5688 (-1.26) 1.4243*** (3.24) 3.7868*** (2.85) 0.052 8740

Vlm

USD mandated vs. EUR mandated

64.8541*** (30.44) 0.033 8821

-4.2991*** (-2.91) 22.4662*** (3.29) -6.4935** (-2.37) 4.0289 (1.16)

-9.9779*** (-4.08) 22.1282*** (3.23) -8.7110** (-2.13) 4.1796 (1.14) 0.8681*** (3.54) 0.7111* (1.77) 1.2470 (0.03) -130.6328** (-2.37) -1.2489 (-0.12) 26.6882*** (3.40) -4.2657 (-1.10) 3.8056 (0.72) 57.3357*** (4.77) 0.047 8740

Activity variables Ntrades Ntrades

23.0793*** (100.95) 0.013 8821

0.0583 (0.27) 1.4968** (2.26) -1.1243*** (-3.06) 1.1234** (2.11)

Nparties

-0.9382** (-2.53) 1.4418** (2.16) -1.1673** (-2.62) 1.1336** (2.10) 0.1319*** (3.07) 0.1234** (2.64) 8.4060 (1.02) -13.2524** (-2.11) -1.2970 (-0.55) 2.7758*** (3.05) 0.4030 (0.53) 0.5573 (0.61) 18.8475*** (13.31) 0.029 8740

Nparties

Table 3: Panel difference-in-difference specification (fixed effects). We show estimation results of equation (4), where the treatment group are the USD mandated contracts and the control group are the EUR mandated contracts. The dispersion metrics and the Amihud measure are defined in equations (1), (2) and (3) respectively. V lm is the amount of gross notional traded in US dollars, N trades is the number of trades executed and N parties is the number of unique counterparties active on a given day. Date(1) is a dummy variable that takes the value of 1 after the introduction of SEF trading on 2 October 2013 and Date(2) is a dummy variable that takes the value of 1 after the mandate effective dates as per Table 1. Curr is a dummy that takes the value 1 for USD-denominated contracts and is zero otherwise. VIX and VDAX are the S&P 500 and DAX volatility indices and log RSP 500 and log RDAX are the daily log returns on the indices themselves. O/N Spread USD and O/N Spread EUR are the differences between the overnight unsecured borrowing rates and the respective central bank rates. Slope USD and Slope EUR are the spreads between the 10-year and 3-month government securities of the US and the investment grade Eurozone countries respectively. The model is estimated using maturity and currency fixed effects. Standard errors are clustered by maturity and currency. Robust t-statistics are shown in the parentheses. *, ** and *** denote significance at 10%, 5% and 1% levels respectively. The time period is January 1, 2013 to September 15, 2014.

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Within-R2 N

Constant

Slope USD

O/N Spread USD

log RSP 500

VIX

M AT × Date(2)

Date(2)

M AT × Date(1)

Date(1)

0.8441*** (65.67) 0.065 5875

-0.2220*** (-3.87) 0.0261 (0.41) 0.0304 (1.73) -0.0589** (-2.63)

Disp (vw) -0.2301*** (-4.25) 0.0227 (0.36) 0.0605*** (3.19) -0.0576** (-2.62) 0.0278*** (6.17) -2.8813*** (-4.77) -0.1680 (-0.98) -0.0085 (-0.28) 0.4681*** (3.94) 0.088 5861

Disp (vw)

1.0916*** (69.89) 0.065 5875

-0.3372*** (-6.37) 0.0799 (1.33) 0.0582** (2.18) -0.0704** (-2.23)

-0.3288*** (-6.05) 0.0780 (1.32) 0.0865*** (3.02) -0.0690** (-2.21) 0.0333*** (6.23) -3.2104*** (-4.42) -0.3533 (-1.68) -0.0578 (-1.43) 0.7406*** (5.06) 0.084 5861

Liquidity variables Disp (JNS) Disp (JNS)

15.1952*** (24.26) 0.137 5090

-10.7954** (-2.24) 8.8217* (1.79) 3.7241* (1.94) -3.5602 (-1.62)

Amihud -9.9152** (-2.27) 8.7920* (1.80) 3.5274** (2.19) -3.5700 (-1.63) 0.1261 (0.76) 4.7511 (0.30) -6.1576 (-0.95) -2.1722 (-1.43) 17.9719*** (3.26) 0.146 5090

Amihud

4.6513*** (14.97) 0.049 5875

0.1963 (1.64) 1.9100** (2.43) -0.0645 (-0.30) -0.3813 (-1.05)

Vlm

-0.4108 (-1.66) 1.8953** (2.43) 0.2702 (1.13) -0.3731 (-1.03) 0.1396*** (3.16) -11.1654** (-2.29) 1.0264 (0.77) 1.1075** (2.69) 0.3715 (0.22) 0.062 5861

Vlm

USD mandated vs. USD non-mandated

62.1412*** (20.93) 0.031 5875

0.5327 (1.24) 17.6343** (2.61) 0.2127 (0.41) -2.6773 (-1.20)

-5.2531** (-2.36) 17.3370** (2.59) 3.5493** (2.76) -2.5792 (-1.16) 1.8043** (2.82) -149.5849 (-1.75) -16.8542 (-1.11) 8.7058** (2.19) 18.3383 (1.04) 0.044 5861

Activity variables Ntrades Ntrades

20.9446*** (62.82) 0.013 5875

-0.5161*** (-3.91) 2.0712*** (3.20) -0.1081 (-0.45) 0.1072 (0.23)

Nparties

-1.8809*** (-5.79) 2.0432*** (3.16) 0.4925* (2.03) 0.1226 (0.27) 0.2253*** (6.08) -18.8083** (-2.19) -1.3618 (-0.55) 2.2894*** (3.92) 12.9701*** (7.67) 0.032 5861

Nparties

Table 4: Panel difference-in-difference specification (fixed effects). We show estimation results of specification (5), where the treatment group consists of the USD mandated contracts and the control group of the USD non-mandated contracts. The dispersion metrics and the Amihud measure are defined in equations (1), (2) and (3) respectively. V lm is the amount of gross notional traded in US dollars, N trades is the number of trades executed and N parties is the number of unique counterparties active on a given day. Date(1) is a dummy variable that takes the value of 1 after the introduction of SEF trading on 2 October 2013 and Date(2) is a dummy variable that takes the value of 1 after the mandate effective dates as per Table 1. MAT is a dummy that takes the value 1 for mandated contracts and is zero otherwise. VIX is the S&P 500 volatility index and log RSP 500 is the log daily return on the index itself. O/N Spread USD is the difference between the overnight unsecured borrowing rate and the respective central bank rate. Slope USD is the spreads between the 10-year and 3-month Treasury securities. The model is estimated using maturity fixed effects. Standard errors are clustered by maturity. Robust t-statistics are shown in the parentheses. *, ** and *** denote significance at 10%, 5% and 1% levels respectively. The time period is January 1, 2013 to September 15, 2014.

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R2 N Specification

Constant

Slope EUR

Slope USD

O/N Spread EUR

O/N Spread USD

VDAX

VIX

log RDAX

log RSP 500

Date(1)

SEF

-0.1187*** (-4.21) -0.2132*** (-5.86) -0.9784 (-1.05) 1.1575* (1.87) 0.0242*** (5.42) -0.0059 (-1.29) 0.0381 (0.25) 0.4167*** (6.53) -0.1398*** (-4.51) -0.0022 (-0.05) 1.1132*** (8.89) 0.070 8316 FE

Disp (vw) -0.1195*** (-2.97) -0.3188*** (-6.27) -2.1289** (-2.29) -2.4488** (-2.15) 0.0219*** (4.03) 0.0067 (1.54) 0.5575** (2.42) 0.6491*** (6.04) -0.1727*** (-3.56) -0.0214 (-0.30) 1.5407*** (9.60) 0.058 8316 FE

Disp (JNS) -1.1838*** (-3.12) -0.7600* (-1.90) 7.1627 (1.54) 2.7685 (0.77) 0.1017* (1.78) -0.0030 (-0.04) 0.1891 (0.19) -1.1741* (-2.08) 0.3501 (0.55) -2.8336** (-2.63) 11.6262*** (8.11) 0.116 7387 FE

Amihud 1.3734** (2.62) -0.4560** (-2.21) -2.9168 (-0.63) -4.8869 (-1.13) 0.0541* (1.97) 0.0404 (1.32) -0.7651 (-0.63) 2.1614*** (3.85) -0.6945 (-1.41) 1.8431*** (3.78) 3.5852** (2.67) 0.026 8316 FE

Vlm 15.5799** (2.69) -9.5808*** (-2.95) -6.8581 (-0.14) -119.9649** (-2.18) 1.0927*** (2.99) 0.4552 (1.27) -3.9332 (-0.36) 26.9385*** (3.74) -6.9338 (-1.70) 13.2542*** (2.97) 48.6817*** (4.19) 0.027 8316 FE

Ntrades 1.6590** (2.74) -1.3354*** (-3.94) 3.7181 (0.55) -11.1949 (-1.65) 0.1261** (2.74) 0.1144** (2.44) -1.1245 (-0.47) 2.7742*** (3.35) 0.2689 (0.35) 1.1587 (1.40) 18.6906*** (16.11) 0.022 8316 FE

Nparties -0.3077*** (-5.39) -0.0857** (-2.19) -2.3924*** (-2.74) 1.5829** (2.57) 0.0223*** (4.98) -0.0037 (-0.83) 0.2317 (1.23) 0.4091*** (7.51) -0.1181*** (-3.94) -0.0069 (-0.21) 1.0700*** (13.55) 0.0602 7535 FE & IV

Disp (vw)

-0.3910*** (-4.40) -0.1467** (-2.41) -4.2694*** (-3.14) -1.6626* (-1.73) 0.0198*** (2.84) 0.0079 (1.15) 0.7659*** (2.62) 0.6459*** (7.62) -0.1373*** (-2.94) -0.0451 (-0.88) 1.5178*** (12.35) 0.0496 7535 FE & IV

Disp (JNS)

-3.6329*** (-8.89) 0.5922** (2.20) 9.1158 (1.53) 0.7966 (0.19) 0.1155*** (3.76) -0.0089 (-0.29) 1.8161 (1.36) -0.7670** (-2.17) 0.4462** (2.23) -3.0147*** (-13.95) 11.6580*** (21.74) 0.0777 6714 FE & IV

Amihud

4.0384*** (7.56) -2.0579*** (-5.61) -10.8112 (-1.32) -2.8460 (-0.49) 0.0553 (1.32) 0.0240 (0.58) 0.2081 (0.12) 2.5476*** (4.99) -0.8365*** (-2.98) 2.3471*** (7.61) 3.5571*** (4.81) 0.0076 7535 FE & IV

Vlm

42.0938*** (7.63) -25.7008*** (-6.78) -86.4967 (-1.02) -97.0765 (-1.63) 1.0494** (2.42) 0.2630 (0.61) 1.1789 (0.06) 32.0757*** (6.09) -9.5411*** (-3.29) 19.3226*** (6.06) 51.6001*** (6.76) 0.0110 7535 FE & IV

Ntrades

Table 5: SEF trading panel regressions. We show the estimation results of specification (6) for USD and EUR-denominated mandated contracts. The dispersion metrics and the Amihud measure are defined in equations (1), (2) and (3) respectively. V lm is the amount of gross notional traded in US dollars, N trades is the number of trades executed and N parties is the number of unique counterparties active on a given day. SEF is the percent of the total trading volume executed on a SEF, Date(1) is a dummy variable that takes the value of 1 after the introduction of SEF trading (2 October 2013), VIX and VDAX are the S&P 500 and DAX volatility indices and log RSP 500 and log RDAX are the daily log returns on the indices themselves. O/N Spread USD and O/N Spread EUR are the differences between the overnight unsecured borrowing rates and the respective central bank rates. Slope USD and Slope EUR are the spreads between the 10-year and 3-month government securities of the US and the investment grade Eurozone countries respectively. The model is estimated using maturity and currency fixed effects. The top panel shows the results of fixed effects specifications and the bottom panel shows the results of instrumental variable fixed effects specifications where SEF is treated as endogenous and is instrumented using own lags. Standard errors are clustered by maturity and currency. Robust t-statistics are shown in the parentheses. *, ** and *** denote significance at 10%, 5% and 1% levels respectively. The time period is January 1, 2013 to September 15, 2014.

2.4658*** (3.02) -1.8113*** (-3.23) -2.6194 (-0.21) -9.0646 (-1.03) 0.1094* (1.70) 0.1345** (2.12) 0.7409 (0.28) 2.8789*** (3.70) 0.4588 (1.07) 1.2136** (2.58) 18.6155*** (16.49) 0.0219 7535 FE & IV

Nparties

Table 6: Market structure panel difference-in-difference specification (fixed effects). We show estimation results of specifications (7). The dependent variable is always the percentage of dealerto-dealer (D2D) volume over total volume. In the first two specifications, the treatment group consists of the USD mandated contracts and the control group of the EUR mandated contracts. In the next two specifications, the control group consists of USD non-mandated contracts. Date(1) is a dummy variable that takes the value of 1 after the introduction of SEF trading on 2 October 2013 and Date(2) is a dummy variable that takes the value of 1 after the mandate effective dates as per Table 1. Curr is a dummy that takes the value 1 for USD-denominated contracts and is zero otherwise. MAT is a dummy that takes the value 1 for mandated contracts and is zero otherwise. log RSP 500 and log RDAX are the daily log returns of the S&P 500 and DAX indices and VIX and VDAX are estimates of the implied volatility of these indices. O/N Spread USD and O/N Spread EUR are the differences between the overnight unsecured borrowing rates and the respective central bank rates. Slope USD and Slope EUR are the spreads between the 10year and 3-month government securities of the US and the investment grade Eurozone countries respectively. The model is estimated using maturity and currency fixed effects. Standard errors are clustered by maturity and currency. Robust t-statistics are shown in the parentheses. *, ** and *** denote significance at 10%, 5% and 1% levels respectively. The time period is January 1, 2013 to September 15, 2014. Dealer-to-dealer (D2D) Volume

USD MAT vs EUR MAT Date(1) Curr × Date(1)

-0.0577*** (-7.17) 0.0140 (1.00)

-0.0499*** (-4.88) 0.0182 (1.26)

M AT × Date(1) Date(2) Curr × Date(2)

-0.0981*** (-9.08) 0.0161 (0.84)

-0.0884*** (-5.92) 0.0121 (0.63)

M AT × Date(2) log RSP 500 log RDAX VIX VDAX O/N Spread USD O/N Spread EUR Slope USD Slope EUR Constant R2 N

0.6344*** (126.44) 0.102 8694

0.0615 (0.14) 0.3772 (1.44) 0.0046* (1.89) -0.0027 (-1.14) 0.0826 (0.70) 0.0310 (1.06) -0.0475*** (-3.04) 0.0212 (0.85) 0.6895*** (10.38) 0.107 8614

USD MAT vs USD non-MAT 0.0031 (0.12)

0.0174 (0.53)

-0.0468 (-1.62) -0.1262*** (-7.14)

-0.0450 (-1.47) -0.0914*** (-5.58)

0.0441* (1.86)

0.0441* (1.82) 0.4424 (0.55) 0.0051 (0.02) 0.0082** (2.65) -0.0045 (-1.42) 0.5386*** (3.25) 0.0791* (1.79) -0.0771*** (-3.09) 0.0842** (2.93) 0.5879*** (6.07) 0.075 5678

0.5829*** (80.49) 0.061 5740

35 Staff Working Paper No. 580 January 2016

Table 7: Fragmentation panel regressions. We show the estimation results of specification (8) for EUR-denominated contracts. The dispersion metrics and the Amihud measure are defined in equations (1), (2) and (3) respectively. V lm is the amount of gross notional traded in US dollars, N trades is the number of trades executed and N parties is the number of unique counterparties active on a given day. fragm is one minus the percentage of volume traded between US and nonUS counterparties, Date(1) is a time dummy that takes the value 1 after the introduction of SEF trading on 2 October 2013, log RSP 500 and log RDAX are the daily log returns of the S&P 500 and DAX indices and VIX and VDAX are estimates of the implied volatility of these indices. O/N Spread USD and O/N Spread EUR are the differences between the overnight unsecured borrowing rates and the respective central bank rates. Slope USD and Slope EUR are the spreads between the 10-year and 3-month government securities of the US and the investment grade Eurozone countries respectively. The model is estimated using maturity fixed effects. Standard errors are clustered by maturity. Robust t-statistics are shown in the parentheses. *, ** and *** denote significance at 10%, 5% and 1% levels respectively. The time period is January 1, 2013 to September 15, 2014.

fragm Date(1) log RSP 500 log RDAX VIX VDAX O/N Spread USD O/N Spread EUR Slope USD Slope EUR Constant R2 N

Disp (vw)

Disp (JNS)

Amihud

Vlm

Ntrades

Nparties

-0.5964 (-0.90) -0.3866*** (-3.94) -3.9949 (-0.60) -9.8511 (-1.06) 0.1112 (1.27) -0.1058 (-1.21) 0.4546 (0.90) 0.3861*** (3.30) -0.4361 (-1.15) 1.5835 (1.23) 2.7965** (2.15) 0.003 5749

-0.4729 (-0.81) -0.8235* (-2.14) 0.4508 (0.35) -19.2031 (-1.70) 0.1714 (1.13) -0.1859 (-1.00) 3.2035 (1.67) 0.7816*** (3.65) -0.1518 (-1.61) 1.8480 (1.56) 8.1578* (1.91) 0.010 5749

0.1609 (0.39) -2.0481*** (-5.56) 10.0129* (2.09) 3.9104 (1.19) 0.0911 (1.52) 0.0257 (0.40) 0.0971 (0.08) -0.2917 (-0.68) 0.9323 (1.02) 0.1223 (0.11) 15.2246*** (6.09) 0.215 5178

-2.0472*** (-4.69) 0.0016 (0.01) 6.8007 (1.38) -2.3227 (-0.54) 0.0666** (2.53) -0.0369* (-2.04) -3.2532*** (-3.39) 1.7321** (2.51) -1.3911** (-2.34) 1.3761** (2.47) 6.3808*** (8.77) 0.036 5749

-3.2927** (-2.97) -3.3319** (-2.95) 107.6561*** (3.33) -56.2627 (-1.43) 0.8843*** (3.11) -0.3495 (-1.62) -24.0778*** (-3.07) 10.4110 (1.76) -10.0635* (-2.06) 7.7884 (1.73) 42.0813*** (14.51) 0.041 5749

-1.3109** (-2.63) 0.2955 (1.05) 27.0525*** (3.10) -7.5684 (-1.01) 0.1538*** (3.24) 0.0391 (0.68) -6.5624** (-2.76) 1.7546 (1.05) -1.6763** (-2.35) 1.2913 (1.00) 17.7457*** (22.58) 0.024 5749

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