1 Secondary Transaction Costs in Bonds Larry Harris.
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Transcript of 1 Secondary Transaction Costs in Bonds Larry Harris.
1
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.
3
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?
7
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.
8
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.
10
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.
11
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
12
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.
13
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
17
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
19
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.
20
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
23
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.
25
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
30
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
32
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
34
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
35
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.
36
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.
37
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
38
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.
39
Results
40
Mean Estimated Municipal Transaction Costs (Figure 1)
41
Mean Estimated Corporate Transaction Costs (Figure 1)
42
Alternative Cost Functions(Municipal Figure 2)
43
By Trading Activity (Muni’s)
44
By Trading Activity (Corp’s)
45
By Credit Quality (Muni’s)
46
By Credit Quality (Corp’s)
47
By Issue Size (Muni’s)
48
By Issue Size (Corp’s)
49
By Bond Complexity (Muni’s)
50
By Time Since Issuance (Muni’s)
51
By Time To Maturity (Muni’s)
52
By Transparency (Corp’s)
53
Cross-sectional Regressions
54
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.
55
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.
56
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.
57
Regressors
• Inverse Price
– Fixed costs (clearing?)
• Credit Rating Index
• Complexity Features
• Age/Maturity Features
• Size/Scale Features
58
Municipal Results From Table 3, $100,000 Trade Size
59
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)
62
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
64
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
66
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)
67
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
69
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)
71
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)
72
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.
73
Time-series Analysis of Corporate Transparency
74
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
76
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.
77
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
78
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.
79
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
80
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.
81
Learning about Transparency
82
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.
83
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.
84
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.
85
Conclusion
86
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.
87
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!
88
An Important Additional Argument
• Fair valuation of bond funds will be improved by greater transparency.
89
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.
90
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.
91
Time for more sunshine!