secondary transaction costs in bonds
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Secondary Transaction Costs in Bonds. Larry Harris. Formal Disclaimer. The Securities and Exchange Commission, as a matter of policy, disclaims responsibility for any private publication or statement by any of its employees. - PowerPoint PPT PresentationTRANSCRIPT
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Secondary Transaction Costs in Bonds
Larry Harris
2
Formal Disclaimer
• The Securities and Exchange Commission, as a matter of policy, disclaims responsibility for any private publication or statement by any of its employees.
• The views expressed herein are those of the author and do not necessarily reflect the views of the Commission or of the authors’ colleagues upon the staff of the Commission.
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Secondary Bond Markets
• Corporate bonds.
• Municipal bonds.
• Government bonds.
4
Bond Market Characteristics
• Many securities.
• Infrequently traded.
• Almost no contemporaneous price transparency.
• Almost no quotes.
5
The Main Policy Issue
• How does market opacity affect liquidity?
– New car dealer comparison.
– Comparison to equity markets.
6
Important Issues
• What are secondary transaction costs in the bond markets?
• What determines these costs?
– How does bond complexity affect these costs?
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The Research Program
• Examine all municipal (MSRB) and corporate (TRACE) bond trades.
• Measure average transaction costs for each bond.
• Identify cross-sectional determinants of these costs.
• Identify how costs change when bond trades become more transparent.
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The Samples
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The MRSB Sample
• Broker-dealers report all municipal bond trades to the MRSB.
– Price, time, size, dealer, customer side.
– Our one-year sample period:November 1999 – October 2000.
• These data are now available on the next day on the Internet.
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The TRACE Sample
• Broker-dealers report all corporate bond trades to the NASD.
– Price, time, size, dealer, customer side.
– Our one-year sample period:January 2003 – December 2003.
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MRSB Sample Selection (from Section 3.1)
Bonds TradesVolume
($ billion)
Records 463,346 7,024,678 2,575
Final sample 167,851 5,399,283 832
Deleted:Unknown securities DerivativesVariable rate bonds Missing data Unidentified cost Pricing errors regressions
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TRACE Sample Selection (from Table 1)
Bonds TradesVolume
($ billion)
Records 68,877 8,668,987 9,413
Final sample 16,746 6,649,758 5,079
Same deletion criteria as applied to the MSRB sample.
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MSRB Bond Characteristics
Mean 1st
pctl 99th pctl
Trades per week 0.6 <0.1 5.4
Dollar trade size ($000)
Minimum 16 2 105
Median 73 5 992
Maximum 977 10 11,199
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TRACE Bond Characteristics(from Table 2)
Mean 1st
pctl 99th
pctl
Trades per day 1.9 <0.1 22
Dollar trade size ($000)
Minimum 32 0.4 545
Median 584 5.4 6,806
Maximum 12,401 26 105,243
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MSRB Characteristics (from Table 1, Panel B)
Bonds TradesValue traded
Superior AA/AAA 74% 77 78
Other inv. quality 8 10 9
Speculative <BBB < 1 < 1 < 1
Missing 18 13 13
Credit Quality
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TRACE Characteristics (from Table 1, Panel B)
Bonds TradesValue traded
Superior AA/AAA 9% 8 8
Other inv. quality 63 63 57
Speculative <BBB 23 26 31
Defaulted 3 2 2
Not Rated 3 1 2
Credit Quality
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Municipal Bond Complexity Features
• Callable
• Sinking fund
• Extraordinary call
• Nonstandard interest payment frequency
• Nonstandard interest accrual method
• Credit enhanced
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MSRB Characteristics (from Table 1, Panel D)
Bonds TradesValue traded
Simple 0 14% 10 14
Typical 1-2 65 54 56
Complex 3+ 21 36 29
Bond Complexity
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MRSB Transparency
• During most of the sample period, bond trades were made public on the next day if the bond traded four times.
• Transparency and trade activity therefore are correlated.
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Corporate Transparency
• NYSE ABS bond trades are completely transparent.
• Trades for TRACE-transparent bonds were reported with a 45 minute lag.
• Bonds have been made TRACE-transparent based on credit quality and original issue size (IOS).
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TRACE-Transparent Bonds
• Throughout 2003: All bonds rated A and above with original issue size>$1B.
• March 1, 2003: All bonds rated A and above with $100M>OIS>$B.
• April 14, 2003: 120 bonds rated BBB with stratified original issue sizes.
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2003 Corporate Transparency (from Table 1, Panel D)
Bonds TradesValue traded
TRACE (any time) 22% 49 53
ABS-listed 3 5 3
ABS and TRACE 1 2 2
Never transparent 76 48 45
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Transaction Cost Measurement Methods
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Benchmark Methods
• Most transaction cost measures require price benchmarks.
– Quotes
– Average price: Warga and others
– Closing or opening prices
• Without benchmarks, we must use econometric methods.
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Econometric Approaches
• Bid/ask bounce is due to transaction costs.
–Measure the bounce.
• The Roll Serial covariance spread estimator.
• Regression methods useful when we know the side trade initiators (customers) are on.
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A Constructive Introduction to Our Econometric Method
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Price and Value
• Log Price = Log Value +/- trade cost
• Let Qt indicate with values 1, or -1 whether trade t was initiated by a customer buyer or seller.
log logt t t tP V c Q
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Add Interdealer Trades
• Let It indicate with values 1 or 0 whether trade t was an interdealer trade.
• Set Qt to 0 for interdealer trades.
• Let t be the unknown interdealer price impact. log logt t t t t tP V c Q I
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Let Cost Vary with Size
• An average response function plus a random error.
t t tc c S
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Bond Transaction Returns
• Log price change between trades t and s produces a regression equation. (The trades need not be in order.)
P Vts ts
t t s s
D Dt t s s t t s s
r r
c S Q c S Q
Q Q I I
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Model Value Returns
• Bond value returns have drift, common, and idiosyncratic components.
• Random in bond-specific value.
5%Vts ts
Avg ts Dif ts
st
r Days CouponRate
SLAvg SLDif
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The Cost Function
• Municipal bonds:
• Corporate bonds:
0 1 2
1logt t
t
c S c c c SS
20 1 2 3 4
1logt t t t
t
c S c c c S c S c SS
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The Regression Model
Combining terms gives
0 1
2
5%
log log
Pts ts
SLAvg ts SLDif ts
t st s
t s
t t s s
ts
r Days CouponRate
SLAvg SLDif
Q Qc Q Q c
S S
c Q S Q S
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The Error Term
has variance
where Dts =0, 1, or 2 counts the interdealer trades among trades t and s.
D Dts ts t t s s t t s sQ Q I I
2 2 2 22Sessionsts ts Sessions ts tsN D D
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Estimation Strategy
• Estimate the model without the indices for each bond.
• Adjust prices to remove trade costs.
• Use repeat sales methods to compute the indices.
– Involves weighted regressions.
• Re-estimate the model with the indices.
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Weighted Least Squares
• Estimate the model with OLS for each bond.
• Use pooled constrained WLS to regress the squared residuals on independent variables to estimate the variance components.
• Re-estimate the model with WLS.
• Iterate until convergence.
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Cost Estimates
• Estimated cost for a given size is
• The estimate error variance is
0 1 2
1logc S c c c S
S
1
1 1ˆ1 log
log
cVar c S SS S
S
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Mean Cost Estimates
• Compute weighted means across bonds. For weights, use estimates of the precision of the cost estimate (inverse estimator error variance).
• The data thus tell us where the information is.
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Results
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Mean Estimated Municipal Transaction Costs (Figure 1)
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Mean Estimated Corporate Transaction Costs (Figure 1)
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Alternative Cost Functions(Municipal Figure 2)
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By Trading Activity (Muni’s)
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By Trading Activity (Corp’s)
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By Credit Quality (Muni’s)
46
By Credit Quality (Corp’s)
47
By Issue Size (Muni’s)
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By Issue Size (Corp’s)
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By Bond Complexity (Muni’s)
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By Time Since Issuance (Muni’s)
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By Time To Maturity (Muni’s)
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By Transparency (Corp’s)
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Cross-sectional Regressions
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Cross-sectional Regressions
• Cross-sectional regression analyses help isolate effects by disentangling conflicting effects.
• Dependent variable: Average bond transaction cost estimate for a representative trade size.
• Estimate the models with WLS.
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Information Considerations
• The dependent variable observations are noisy estimates for which we have estimates of the estimator error variances.
• The model should have an independent, equal variance error term.
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Regression Weights
• Obtain OLS residuals.
• Regress OLS squared residuals on a constant and on the error variances to obtain predicted variances.
• Use the inverse of the predicted variances as weights for the WLS analysis.
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Regressors
• Inverse Price
– Fixed costs (clearing?)
• Credit Rating Index
• Complexity Features
• Age/Maturity Features
• Size/Scale Features
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Municipal Results From Table 3, $100,000 Trade Size
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Inverse Price and Credit Rating Coefficients
Regressor Estimate t-stat
Intercept (bps) 14 4
Inverse price 4524 77
Credit quality index -2.1 -33
Missing credit rating -47 -30
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A Quick Digression
• Credit is missing for 18 percent of the bonds. We set the credit quality index to 0 and the missing credit dummy to 1.
• The missing credit coefficient should equal the average (missing) credit quality index times the credit quality index coefficient.
• The implied average credit quality index is 47÷ 2.1 = 22+.
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Regressor Estimate t-stat
Callable 23 95
Sinking fund 15 54
Extraordinary call 9 40
Nonstandard int pmt freq 2 4
Nonstandard int accrual 9 7
Credit enhanced 11 44
Complexity Coefficients(in bps)
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Regressor Estimate t-stat
Time since issuance 3 57
Time to maturity 16 130
Pre-refunded -31 -95
Super sinker -33 -13
Age/Maturity Coefficients
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Regressor Estimate t-stat
Value of the bond 0.9 10
Value of all bonds by the same issuer
-0.2 -2.6
Value of all bonds in the same state
-2.3 -9.0
State bond demand index 3.6 14.5
Adjusted R2 50%
Size/Scale Coefficients
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Other Municipal Results (From Table 3)
• Generally similar results for other trade sizes.
• However, some evidence that institutional investors are less adversely affected by instrument complexity than retail investors.
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Corporate ResultsFrom Table 5, $100,000 Trade Size
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Regressor Estimate t-stat
Rating is BBB 4 7.0
Rating is B or BB 6 6.8
Rating is C and below 10 6.6
Bond is in default 8 2.3
Credit Rating Coefficients(in bps)
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Regressor Estimate t-stat
Coupon rate (in percent) 3.1 17
Average price (in % of par) -1.9 -51
Convertible to stock 30 bps 20
Additional Risk Coefficients
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Regressor Estimate t-stat
Years since issuance (square root)
5 17
Years to maturity (square root)
16 76
Bond soon to be called
-40 bps -10
Sinking fund -13 bps -3
Maturity and Age Coefficients
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Regressor Estimate t-stat
Issue size (sq. root of millions)
-0.16 -6
Total other issues by same issuer (sq. root of millions)
0.07 22
Size Coefficients
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Regressor Estimate t-stat
Attached call -11 -12
Attached put -44 -26
Floating rate -12 -6
Variable rate 6 3
Nonstandard accrual 7 6
Maturity date extended or extendable
5 5
Some Complexity Coefficients(in bps)
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Regressor Estimate t-stat
TRACE-transparent (fraction of trades reported to public during 2003)
-3.8 -4.2
Listed on NYSE ABS -3.5 -2.0
Transparency Coefficients(in bps)
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Corporate Cost Determinants (From Table 5)
• Generally similar results for other trade sizes.
• Transparency has the least effect in the smallest and largest trade sizes.
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Time-series Analysis of Corporate Transparency
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Transparency Changes
• All 3,004 bonds rated A and up with $100M<original issue size<1B became TRACE-transparent on March 1, 2003.
• A size-stratified sample of 120 intermediate sized BBB rated bonds became transparent on April 14.
• What happened to costs?
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Samples
Comparison Samples
Target C1 C2 C3
Original issue size
>$100M&<1B
>$1B <$100M >$100M&<1B
Rating A & up A & up A & up BBB
Transparent March 1 Always Never Never
Bonds 3,004 814 8,952 4,065
Trades (thousands)
952 1,516 1,014 1,219
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Time-series Method
• For each sample, use a regression model to estimate a different pooled average cost response function for each day.
• Simultaneously estimate a common factor return using repeat sales index estimation method.
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Sketch of Time-series Model
1
5%
Pts ts
t t t s s s
t
J tsJ s
r Days CouponRate
c S Q c S Q
r
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Difference of Differences Comparison Method
• On each day, compute difference in costs between the March 1 sample and the three control samples.
• Compare the average cost differences before and after March 1.
• Use time-series sample variances to construct t-statistics.
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Results for $100K Trade Size(from Table 6)
ComparisonDifference of differences t-statistic
T minus C1 -10 -9
T minus C2 -11 -9
T minus C3 -14 -12
C1 minus C2 -1 -1.5
C2 minus C3 -3 -3.9
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More Results
• Similar results for other trade sizes.
• Similar, but smaller, results for the 120 BBB bonds.
– -5 and -7 bps versus two comparison samples, both statistically significant.
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Learning about Transparency
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Diffusion of Impact
• The results underestimate the long run benefits of transparency because many were unaware that prices were available.
• Obtaining last trade prices was—and is still—difficult.
• These observations probably explain why the BBB effect is smaller.
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A Back of the Envelope Calculation
• Cross-sectional effect at $100K trade size: -3.8 bps for TRACE-transparent and -3.5 for ABS-listed.
• Time-series effect: -10, -11, -15 bps for versus various comparisons for the March 1 bonds, and -5 and -7 for the BBB bonds.
• Safe to say minimum -5 bps.
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A Back of the Envelope Calculation
• About $2 trillion 2003 volume in non-transparent corporate bonds.
• 5 bps of $2 trillion is one billion dollars.
• The estimate is not unrealistic in comparison to total dealing profits.
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Conclusion
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Summary
• Municipal and corporate bonds are expensive to trade.
• Retail investors, and perhaps even issuers, could benefit if issuers issued simpler bonds.
• Studies such as this one are essential inputs into the regulatory process.
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A Final Perspective
• A corporate bond can be hedged by a portfolio of Treasury bonds and the issuer’s stock.
• Both trade in fully price-transparent markets!
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An Important Additional Argument
• Fair valuation of bond funds will be improved by greater transparency.
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Progress
• As of October 1, trades in 17,000 corporate bonds are available for dissemination within 30 minutes.
• 99 percent of all corporate issues will be TRACE-transparent with a 15-minute lag by July 2005.
• Starting in January 2005, all trades in municipal issues will available in real time with a 15-minute lag.
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Some Predictions
• Retail interest in bonds will surge.
• New trading systems will emerge.
• Volumes will increase.
• Dealers will continue to make money—perhaps more—but it will be more difficult.
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Time for more sunshine!