The FINRA page titled “Independent TRACE Studies” leads me to some studies that have come out of mandatory TRACE reporting for Structured Credit. The data collected are not made public in raw form, but aggregate data is made available on a daily and monthly basis.
Structured Credit is a little far from my bailiwick, but some of the statistics cited are so entertaining I just had to highlight them here.
A 2013 paper by Hendrik Bessembinder, William F. Maxwell and Kumar Venkataraman is titled Trading Activity and Transaction Costs in Structured Credit Products:
After conducting the first study of secondary trading in structured credit products, the authors report that the majority of products did not trade even once during the 21-month sample. Execution costs averaged 24 bps when trades occurred and were considerably higher for products with a greater proportion of retail-size trades. The authors estimate that the introduction of public trade reporting would decrease trading costs in retail-oriented products by 5-7 bps.
An acknowledgement in this paper, by the way, introduced me to the Montreal Institute of Structured Finance and Derivatives, which appears to be funded by agencies of the Province of Quebec and provides modest funding to genuine research – only $300,000 p.a., but that’s not bad when compared to their 10-year total budget of $15-million (meanwhile, here in Ontari-ari-ari-owe, we fund ex-regulators and lawyers at FAIR Canada. Gag.) You learn something new every day!
Anyway, back to Bessembinder et al. and the amazing statistics:
Notably, less than twenty percent of the SCP universe trades at all during the twenty-one month sample period. One-way trade execution costs for SCPs average about 24 basis points. However, trade execution costs vary substantially across SCP categories, from 92 basis points for CBOs to just 1 basis point for TBAs. We show that trading costs depend in particular on what we term the product’s “customer profile,” which depends on issue size and the proportion of Retail- versus Institutional-size trades. Sub-products with an Institutional Profile tend to have lower costs. The highest average trading costs are observed for Agency CMOs (74 basis points) and CBOs (92 basis points), each of which has a low (22% or less) proportion of large trades. The lowest average trading cost estimates are observed for TBA securities (1 basis point), CMBS (12 basis points), and ABS secured by auto loans and equipment (7 basis points), each of which has a large (54% or greater) percentage of large trades.
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Structured Products (SCPs), including asset-backed (ABS) and mortgage-backed (MBS) securities, comprise one of the largest but least-studied segments of the financial services industry. As of the end of 2012, there was $8.6 trillion outstanding in mortgage-backed securities and $1.7 trillion outstanding in asset-backed securities, implying that the SCPs markets are comparable in size to the $11 trillion U.S. Treasury Security market
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Increased transparency has the potential to reduce the dealer mark-ups or bid-ask spreads, provide more information on the fair price of the security, and improve regulators and customers’ ability to control and evaluate trade execution costs. These ideas have been emphasized by Rick Ketchum, Chairman and CEO of the Finance Industry Regulatory Authority (FINRA):“From the standpoint of investor protection, which is and always will be FINRA’s top priority, we simply must shed more light on the darker areas of the fixed income market.
That last quote from Rick Ketchum illustrates the big problem with securities regulation nowadays. They have swung so far over to the ‘consumer protection’ objective that they have – at least to some degree – lost sight of why capital markets exist in the first place: to get money from savers to those who want to invest in their businesses. I will certainly agree that investor protection is a worthy objective; and I will agree that it is related reasonably closely to the objective of having a well functioning capital market; but for it to be called the “top priority” shows very strange priorities.
So the first amazing statistic is:
The MBS database provided to us by FINRA contains almost 1.1 million distinct securities. The large number of securities reflects that a basic pool of assets may have more than a hundred tranches, each with a unique payoff structure, and assets can be re-securitized (By comparison, less than 5,000 companies were listed on the U.S. equity exchanges at the end of 2012). However, as Panel A of Table 1 shows, many of these issues are very small, as the 25th percentile issue size is less than $2MM. The median issue size is less than $5MM. However, the distribution is positively skewed, as the mean issue size is $22.8MM. The MBS securities are of long average maturity, as shown on Panel B, with a mean maturity close to 19 years.
Holy smokes! I knew there were lots, but I would have guessed ‘under a million’. Hundred-tranche structured products sound pretty amazing, too.
Table 2 reports on trading activity for MBS. Notably, only 17.8% of the issues traded at all during the twenty one month period studied. The mean dollar volume traded across the full sample of MBS securities is $106MM, with an average of only 4.1 trades in each security. Fannie’s issues average six trades during the sample, and the average trading volume for Fannie issues is almost three times as large as for the next most frequently traded issue (Ginnie). Freddie’s issues are traded significantly less than either of the other agencies. Non-Agency issues trade an average of only 1.8 times each, but surprisingly have the largest proportion of issues (23%) that trade at all. Non-Agency issues have an average of 3.5 dealers at issuance, compared to slightly over four dealers for each Agency issue
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Table 3 contains information regarding the ABS data, which contains slightly over 300,000 issues (compared to 1.1 million issues in the MBS universe).5 The ABS issues are larger than MBS issues, with the mean ($114MM) and median ($29MM) issue size each close to five times larger than for ABS. Still, some ABS issues are very small; the 5th percentile of the issue size distribution is only $100,000 for ABS, compared to over $1MM for MBS. ABS issues have an average maturity of 23.2 years, about 5 years longer than MBS products.Panel B of Table 3 reports on trading activity in the ABS market. Like MBS, ABS trade infrequently, but the percentage of issues that trade at all is almost 30%, considerably higher than MBS at 18%. The average number of trades per security is 4.97, but the trades are on average smaller for ABS; the mean cumulative trading volume for ABS is $16.3MM, compared to the $106MM for MBS issues. The likelihood of trading and mean number of trades is surprisingly homogenous across issue size terciles. However, average trade size and cumulative dollar volume is larger for ABS of greater issue size.
And what are these trades?
Table 5 reports on the distribution of trade sizes in SCPs. We consider a trade to be small if it is for less than $100,000 and large if it is for more than $1MM.
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For comparison purposes, we examine the distribution of trade sizes for corporate bonds during the six months before and after the introduction of public transaction dissemination. Our analysis includes 1.9 Million trades in 10,108 corporate bonds phased into TRACE dissemination between January 2003 and March 2011.8 We find that 72% of corporate bond trades are small (less than $100,000), both before and after trades were publicly disseminated. We conclude that, on average, the market for corporate bonds is more similar to the retail-oriented markets for SCPs, including CMOs and MBSs, and is more distinct from the institutionally-oriented markets for CMBS and TBA securities.
And the cost?
The resulting estimates of customer trade execution costs are reported on Table 6. For the full
sample, the estimated average one-way trade execution cost is 24 basis points. Consistent with results previously reported for corporate and municipal bonds, trade execution costs for SCPs decline with trade size, averaging 83 basis points for small trades, 24 basis points for medium-sized trades, and only five basis points for large trades. Trade execution costs also vary depending on trading frequencies. Average costs for the least-heavily-traded tercile of securities are 31 basis points, compared to 28 basis points for the second tercile and 24 basis points for the most frequently traded tercile. The finding that trade execution costs for SCPs decline with trade size mirrors the findings reported for corporate bonds by Edwards, Harris and Piwowar (2007) and Goldstein, Hotchkiss and Sirri (2007) and for municipal bonds by Harris and Piwowar (2006) and Green, Hollifield, and Schurhoff (2007). The overall level of estimated trading costs for SCP is in line with estimates for corporate bonds.Bessembinder, Maxwell and Venkataraman (2006) study institutional trades in corporate bonds, and report average one-way trade execution costs (prior to transaction dissemination) that average 10 to 20 basis points. Schultz (2001) also studies institutional trades in corporate bonds and estimates that trading costs average 27 basis points. Edwards, Harris and Piwowar (2007) study a broader cross-section that includes retail trades, and estimate that one-way trade execution costs for corporate bonds range from 75 basis points for very small trades to 4 basis points for very large trades.
And the effect of TRACE?
We first implement expression (2) for the full set of corporate bonds that became TRACE-eligible in March of 2003, including in the analysis trades executed six months before to six months after the initiation of public trade dissemination. We find that trading costs for corporate bonds were reduced after the introduction of price dissemination by 9 basis points for small trades, 6 basis points for medium trades, and 3 basis points for large trades. These results are quite similar to those reported by Edwards, Harris, and Piwowar (2006), who study the same sample but rely on more complex estimation techniques.
I take issue with the authors when they claim:
These estimates of lower trading costs for SCPs have important implications for security issuers, investors in these products and broker-dealers who supply liquidity. Improved liquidity that is attributable to post-trade price transparency has the potential to affect the valuation of the bonds themselves and lower yield spreads (see Chen, Lesmond and Wei (2007) for evidence from corporate bonds) for SCP issues. Additionally, the cumulative dollar impact of these trading cost reductions is potentially large. In the case of the transparency experiment for corporate bonds, Bessembinder, Maxwell and Venkataraman (2006) estimate annual trading cost reductions of about $1 billion for the full corporate bond market. In addition, they document the existence of “liquidity externalities”, by which improved transparency for some products can lead to improved valuation and lower trade execution costs for related securities.
As I pointed out in an earlier post, a tighter spread between the dealer buy price and dealer sell price does not necessarily indicate “fairer” prices, since the dealer may well quote only stink bids on customer sales so that a profitable re-sale can be executed quickly. This mechanism, if correct, would actually mean that the liquidity-seeker in the chain of trades is paying more for liquidity under TRACE and that both the interim and ultimate liquidity providers are making excess profits (I refer to this as the Shitty Price Hypothesis). The authors do not examine how the execution prices in the secondary market compare with new-issue prices, which renders their conclusion regarding the “improvement” in liquidity dubious.
In addition to this, the putative benefits of TRACE, estimated as “annual trading cost reductions of about $1 billion for the full corporate bond market”, does not make any attempt to compare this with the cost of the programme. And I don’t mean direct costs, either. If the Shitty Price Hypothesis is correct – and it is consistent with the finding of lower trading levels in the Asquith, Covert and Pathak paper, then actual liquidity has decreased, which means issuers will have to pay more for funds, which means that some bricks-and-mortar projects will be abandoned (this link in the chain is the entire basis for central banking policy rates) … and how much does that cost? Huh?
Anyway, the authors told us to “see Chen, Lesmond and Wei (2007) for evidence from corporate bonds”, so let’s look at Chen, Lesmond and Wei (2007) and see what they have to say.
The paper by Long Chen, David A. Lesmond & Jason Wei is titled Corporate Yield Spreads and Bond Liquidity and it turns out that the last named author is from our very own Rotman School of Management at UofT:
We examine whether liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4000 corporate bonds and spanning investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads; and that an improvement of liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond-specific, firm-specific, and macroeconomic variables, and are robust to issuers’ fixed effect and potential endogeneity bias. Our finding mitigates the concern in the default risk literature that neither the level nor the dynamic of yield spreads can be fully explained by default risk determinants, and suggests that liquidity plays an important role in corporate bond valuation.
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The notion that investors demand a liquidity premium for illiquid securities dates back to Amihud and Mendelson (1986). Lo, Mamaysky, and Wang (2004) further argue that liquidity costs inhibit the frequency of trading. Because investors cannot continuously hedge their risk, they demand an ex-ante risk premium by lowering security prices. Therefore, for the same promised cash flows, less liquid bonds will be traded less frequently, have lower prices, and exhibit higher yield spreads. Thus, the theoretical prior is that liquidity is expected to be priced in yield spreads. We investigate bond-specific liquidity effects on the yield spread using three separate liquidity measures. These include the bid-ask spread, the liquidity proxy of zero returns, and a liquidity estimator based on a model variant of Lesmond, Ogden, and Trzcinka (1999). We find that liquidity is indeed priced in both levels and changes of the yield spread.Contemporaneous studies by Longstaff et al. (2004) and Ericsson and Renault (2002) also relate corporate bond liquidity to yield spreads.
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Historically, the lack of credible information on spread prices or bond quotes has been a major impediment in the analysis of liquidity (Goodhart and O’Hara, 1997) and liquidity’s impact on yield spreads. We employ Bloomberg and Datastream to provide our three liquidity estimates. Among them, the bid-ask spread is arguably the most demonstrable measure of liquidity costs, while the percentage of zero returns is increasingly used as a liquidity proxy in a host of empirical studies.2 Despite the clear intuition surrounding the zero return proxy, it is a noisy measure of liquidity, since it is the combination of a zero return and the simultaneous movement of bond price determinants that more properly estimates liquidity costs, not the lack of price changes per se.
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We find a significant association between corporate bond liquidity and the yield spread with each of the three liquidity measures. Depending on the liquidity measure, liquidity alone can explain as much as 7% of the cross-sectional variation in bond yields for investment grade bonds, and 22% for speculative grade bonds. Using the bid-ask spread as the measure, we find that one basis point increase in bid-ask spread is related to 0.42 basis point increase in the yield spread for investment grade bonds, and 2.30 basis point increase for speculative grade bonds.
So I don’t find anything objectionable in the conclusion; I’ve argued in this blog for a long time that liquidity is a major factor in corporate bond yields, far outweighing credit quality considerations. I will, however, point out that their primary liquidity estimator is at least a little suspect:
Data on the quarterly bid-ask quotes are hand-collected from the Bloomberg Terminals. Most quotes are available only from 2000 to 2003. For each quarter, we calculate the proportional spread as the ask minus the bid divided by the average bid and ask price. The bond-year’s proportional bid-ask spread is then calculated as the average of the quarterly proportional spreads. To include as many bonds as possible, we compute the annual proportional spread as long as there is at least one quarterly quote for the year. The bid-ask quotes recorded are the Bloomberg Generic Quote which reflects the consensus quotes among market participants.
I have to point out that Bloomberg quotes are suspect according to the Jankowitsch, Nashikkar and Subrahmanyam paper referenced in an earlier post, with almost half of actual trades executed outside the quote. This doesn’t necessarily mean that the Bloomberg quotation spreads are useless as a liquidity estimator, but it does mean that somebody has to do some work to show that Bloomberg spreads do in fact have a solid relationship to real life (e.g., that if the bid on bond A is less than the bid on bond B, then you can in fact sell B at a higher price than A).
So what it comes down to is that I agree with Bessembinder, Maxwell and Venkataraman that if TRACE does improve liquidity, then this is a good thing, but I will claim that you cannot measure liquidity in a practical way by comparing dealer sell prices with dealer buy prices if the Shitty Price Hypothesis holds.
As it happens, there is a paper by Nils Friewald, Rainer Jankowitschy and Marti G. Subrahmanyamz which seeks to validate the round-trip trading cost as a measure of liquidity, titled Transparency and Liquidity in the Structured Product Market:
We use a unique data set from the Trade Reporting and Compliance Engine (TRACE) to study liquidity effects in the US structured product market. Our main contribution is the analysis of the relation between the accuracy in measuring liquidity and the potential degree of disclosure. We provide evidence that transaction cost measures that use dealer-speci c information can be eciently proxied by measures that use less detailed information. In addition, we analyze liquidity, in general, and show that securities that are mainly institutionally traded, guaranteed by a federal authority, or have low credit risk, tend to be more liquid.
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For example, measuring liquidity based on the round-trip cost uses the most detailed information, i.e., each transaction needs to be linked to a particular dealer, on each side of the trade. Other liquidity metrics, such as the effective bid-ask spread, do not need such detailed trade information for their computation; but, transactions need to be flagged as buy or sell trades. Many alternative liquidity measures rely on trading data as well: However, they use only information regarding the price and/or volume of each transaction. On the other hand, product characteristics or trading activity variables represent simpler proxies, using either static or aggregated data.
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Exploring the various liquidity metrics and focusing on the predictive power of transaction data, we show that simple product characteristics and trading activity variables, by themselves, may not be sufficient statistics for measuring market liquidity. In particular, when regressing state-of-the-art liquidity measures on product characteristics and trading activity variables, we find that the various liquidity measures over significant idiosyncratic information. Thus, dissemination of detailed transaction data, necessary for the estimation of liquidity measures, is of importance in the fixed-income structured product market. However, there is evidence that liquidity measures based on price and volume information alone (e.g., the imputed round-trip cost measure) can explain most of the variation observed in the benchmark measure, which uses significantly more information and certainly runs the risk of compromising the confidentiality of trader identity. In a second set of regressions, we explain the observed yield spreads using various combinations of liquidity variables and nd similar results: Liquidity measures provide higher explanatory power than product characteristics and trading activity variables alone. However, this result is mostly driven by price and volume information. Thus, details regarding the identities of the specific dealers involved with a particular trade or the direction of the trade are not an absolute necessity in terms of their informational value to market participants: Reasonable estimates of liquidity can be calculated based on prices and volumes of individual trades, without divulging dealer-specific information. This is an important result for all market participants, as it provides valuable insights concerning the information content of reported transaction data.
They acknowledge the Bessembinder paper and discuss the differences:
However, our paper is different from Bessembinder et al. (2013) for at least five important reasons, relating to various aspects of liquidity effects in the structure product market: First, while their analysis is based only on one single estimate of liquidity, we, in contrast, rely on a much broader set of liquidity proxies, which allows us to discuss the information contained in measures employing reported data at different levels of detail. Second, while Bessembinder et al. (2013) use a regression based estimate of liquidity, our round-trip cost measure (which serves as our benchmark) reflects the cost of trading more accurately, since it is based on detailed dealer-specific transaction costs, which are straightforward to compute, and does not depend, in any way, on modeling assumptions. Third, in their analysis, they focus solely on customer-to-dealer trades which constitute only a rather small fraction of all trades in the structured product market, whereas our analysis is based on all customer-to-dealer and dealer-to-dealer transactions. Fourth, unlike their study, we analyze different sub-segments (e.g., tranche seniority, issuing authority, credit rating) of the overall market in much more detail. These sub-segments have either turned out to be important in other fixed income markets, or are unique to the structured product market. Finally, a novel contribution of our paper is that we also analyze which of the liquidity measures best serves to explain yield spreads in the securitized product market.
So more particularly:
Thus, we ask how much information should be disseminated to allow for the accurate measurement of liquidity, compared to our benchmark measure using the most detailed information, in particular trader identity and trade direction, which certainly runs the risk of compromising the identities of individual traders or their trading strategies. Therefore, we measure the efficacy of liquidity metrics that require different levels of detail in terms of the information used to compute them. We analyze two aspects of this question, using different sets of regressions: First, we explore to what extent product characteristics, trading activity variables and liquidity measures using less information can proxy for the benchmark measure which is based on all available information. Second, we study which liquidity measures can best explain the cross-sectional differences in yield spreads for our sample.
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Product characteristics are rather crude proxies of liquidity that rely on the lowest level of informational detail of all the categories.13 Thus, product characteristics are typically used as liquidity metrics when there is a limitation on the level of detail in the transaction data. In particular, we use the amount issued of a security measured in millions of US dollars. We presume securities with a larger amount issued to be more liquid, in general. Another important product characteristic is the time-to-maturity, which corresponds to the time, in years, between the trading date and the maturity date of the security. We expect securities with longer maturities (over ten years) to be generally less liquid, since they are often bought by “buy-and-hold” investors, who trade infrequently. We also consider the instrument’s average coupon as a relevant proxy. Despite the ambiguity of the relationship between the coupon and both liquidity and credit risk, we expect that instruments with larger coupons are generally less liquid.Trading activity variables such as the number of trades observed for a product on a given day represent the aggregate market activity.15 Other similar variables that we calculate on a daily basis, for each product, are the number of dealers involved in trading a specific product, and the trading volume measured in millions of US dollars. We expect these variables to be larger, the more liquid the product. On the contrary, the longer the trading interval, which refers to the time elapsed between two consecutive trades in a particular product (measured in days), the less liquid we would expect the product to be.
Note that the Shitty Price Hypothesis negates this last assumption: dealers will set prices so they can exit their positions quickly.
Liquidity measures are conceptually based, and hence, more direct proxies for measuring liquidity, and require transaction information for their computation. However, the level of detail concerning the required information set varies considerably across measures. The liquidity measure that uses the most detailed information and, thus, serves as our benchmark measure, is the round-trip cost measure, which can be computed only if the traded prices and volumes can be linked to the individual dealer; see, e.g., Goldstein et al. (2007). It is defined as the price difference, for a given dealer, between buying (selling) a certain amount of a security and selling (buying) the same amount of this security, within a particular time period, e.g., one day. Thus, it is assumed that in a “round-trip” trade, the price is not affected by changes in the fundamentals during this period. Following the literature, the round-trip trade may either consist of a single trade or a sequence of trades, which are of equal size in aggregate, on each side. The effective bid-ask spread, proposed by Hong and Warga (2000), can be computed when there is information about trade direction available. The effective bid-ask spread is then defined as the difference between the daily average sell and buy prices (relative to the mid-price).
Many other liquidity measures use only the price and/or volume of each transaction, without relying on dealer-specific or buy/sell-side information. A well-known metric proposed by Amihud (2002), and conceptually based on Kyle (1985), is the Amihud measure. It was originally designed for exchange-traded equity markets, but has also become popular for measuring liquidity in OTC markets. It measures the price impact of trades on a particular day, i.e., it is the ratio of the absolute
price change measured as a return, to the trade volume given in US dollars. A larger Amihud measure implies that trading a financial instrument causes its price to move more in response to a given volume of trading and, in turn, reflects lower liquidity. An alternative method for measuring the bid-ask spread is the imputed round-trip cost, introduced by Feldhutter (2012). The idea here is to identify round-trip trades, which are assumed to consist of two or three trades on a given day with exactly the same traded volume. This likely represents the sale and purchase of an asset via one or more dealers to others in smaller trades. Thus, the dealer identity is not employed in this matching procedure; rather, differences between the prices paid for small trades, and those paid for large trades, based on overall identical volumes, are used as the measure. The price dispersion measure is a new liquidity metric recently introduced for the OTC market by Jankowitsch et al. (2011). This measure is based on the dispersion of traded prices around the market-wide consensus valuation, and is derived from a market microstructure model with inventory and search costs. A low dispersion around this valuation indicates that the nancial instrument can be bought for a price close to its fair value and, therefore, represents low trading costs and high liquidity, whereas a high dispersion implies high transaction costs and hence low liquidity. The price dispersion measure is defined as the root mean squared difference between the traded prices and the average price, the latter being a proxy for the respective market valuation.The Roll measure, developed by Roll (1984) and applied by Bao et al. (2011) and Friewald et al. (2012), for example, in the context of OTC markets, is a transaction cost measure that is simply based on observed prices. Under certain assumptions, adjacent price movements can be interpreted as a “bid-ask bounce”, resulting in transitory price movements that are serially negatively correlated. The strength of this covariation is a proxy for the round-trip transaction costs for a particular nancial instrument, and hence, a measure of its liquidity. This measure requires the lowest level of detail as only traded prices, and not trading volume or dealer-specific information, are used in the computation.
Whoosh! That’s a lot of liquidity measures! And I thought I was obsessive!
The descriptive statistics and correlations presented in Section 5.1 provide initial indications of the informational value of the various liquidity measures. When analyzing the liquidity of the different markets and their sub-segments, the liquidity measures offer additional insights compared to the product characteristics and trading activity variables. For example, when comparing the different market segments, higher trading activity is not always associated with lower transaction costs. The correlation analysis hints in the same direction: There is low correlation between the product characteristics and the liquidity measures (the highest correlation coefficient is 0.26 in absolute terms) and between trading activity variables and liquidity measures (less than 0.20 in absolute terms). Thus, it seems that liquidity measures that rely on more detailed transaction data can provide important additional information, based on this perspective.
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Table 10 shows the results for this analysis, presenting the six specifications. In regressions
(1) to (5), we use each of the liquidity measures in turn, plus all trading activity variables and product characteristics, to explain the round-trip costs. When we add just one individual proxy to the regression analysis, we find that the imputed round-trip cost, the effective bid-ask spread and the price dispersion measure are the best proxies, with R2 values of around 50% to 60%, whereas the Amihud and Roll measures slightly increase the R2 to around 40% compared to regressions without liquidity measures. When adding all the liquidity measures to the regression equation, in regression (6), we obtain an R2 of 67%, i.e., the explanatory power increases considerably when we include all these proxies. We consider this level of explanatory power quite high, given the rather diverse instruments with potentially different liquidity characteristics and the low number of trades per security and day, in general. We get similar results (not reported here) when explaining the effective bid-ask spread with liquidity measures using less information. Thus, we find evidence that liquidity measures using more detailed data can be proxied reasonably well by similar measures using less data. We further discuss this issue in the next section and analyze the importance of the disclosure in the context of pricing.
And correlation with yields?
Analyzing the effect of the trading activity variables in the full model, we find economically significant results only for the trading interval: An increase in the trading interval by one standard deviation is associated with an increase in the yield spread of 15 bp. The information contained in the other trading activity variables, e.g., traded volume, seems to be adequately represented by the liquidity measures. However, more important are the results for the product characteristics. The most relevant variable in the full model turns out to be the coupon. A one-standard-deviation higher coupon results in an increase of 137 bp in the yield spread. Thus, the coupon rate has the highest explanatory power of all the variables, indicating that a higher coupon is also associated with higher credit risk for certain products, in particular when there is no credit rating available. The amount issued shows important effects as well, where a one-standard-deviation increase leads to an 19 bp decrease in the yield spread: Larger issues have lower yield spreads. The maturity of a structured product is related to the yield spread as well, indicating that longer maturities are associated with somewhat lower spreads. However, compared with the other product characteristics, the maturity is of minor importance. Overall, the full model has an R2 of 69.9% with significant incremental explanatory power shown by the liquidity measures. Thus, liquidity is an important driver of yield spreads in the structured product market; therefore, the dissemination of trading activity information is important, given the size and complexity of this market.
And they conclude:
Exploring the relation between the various liquidity proxies and the depth of disseminated information, we find that product characteristics or variables based on aggregated trading activity, by themselves, are not sucient proxies for market liquidity. The dissemination of the price and volume of each individual trade is important for the quantification of liquidity effects, particularly for explaining yield spreads. However, we also provide evidence that liquidity measures that use additional dealer-specific information (i.e., trader identity and sell/buy-side categorization) can be efficiently proxied by measures using less information. In our regression analysis, we find that liquidity effects cover around 10% of the explained variation in yield spreads. Thus, the dissemination of trading activity is essential, given the trade volume and complexity of this market. These results are important for all market participants in the context of OTC markets, as it allows establishing an understanding of the information content contained in the disclosure of trading data.