Mizrach/Neely Tatonnement
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Overview of Papers Discussed
Bruce Mizrach and Chris Neely (2006), “The Microstructure of Bond Market Tatonnement,” St.
Louis Federal Reserve Working Paper #2005-70a.
Bruce Mizrach and Chris Neely (2006), “The Transition to ECNs In the Secondary Treasury
Market.” Federal Reserve Bank Review, Nov/Dec., forthcoming.
Oleg Korenok, Bruce Mizrach, and Stan Radchenko (2006), “Structural
Estimation of Information Shares.”
Michael Fleming, Bruce Mizrach, and Chris Neely (2006): “The On-The
Run U.S. Treasury Market: A Complete Snapshot.”
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Outline
I.
II.
III.
IV.
V.
VI.
Conceptual Material on Market Microstructure
Structural Model
Description of Treasury Market Data and Architecture
Estimates of Treasury Market Information
Alternative Bayesian Structural Approach
Conclusion
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I. Market Microstructure Concepts
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Market Microstructure
Hasbrouck: “Market microstructure is the study of trading mechanisms used for financial
securities.”
The growing availability of transactions databases has facilitated the study of high frequency
phenomena in various markets. (Equities: TAQ; NASTRAQ. FX: Olsen;)
The majority of research has been on equities and foreign exchange, much less on fixed income.
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Market Fragmentation
Similar or identical securities often trade in multiple venues.
Hasbrouck: “In all security markets there is a trade-off between consolidation and fragmentation.
Consolidation… brings all trading interest together in one place, thereby lessening the need for
intermediaries, but as a regulatory principle it favors the establishment and perpetuation of a
single market venue with consequent concern for monopoly power. Allowing new market
entrants…maximizes competition among trading venues, but at any given time the trading
interest in a security is likely to be dispersed (fragmented) among the venues, leading to
increased intermediation and price discrepancies among markets.” (Italics added).
This paper: IDB spot versus futures markets in Treasuries.
Mizrach and Neely (2006): IDB voice markets versus electronic markets Treasuries.
Korenok, Mizrach and Radchenko (2006): 6 stocks that have dual listings on NYSE and
Nasdaq.
Fleming, Mizrach and Neely: BrokerTec versus eSpeed ECNs.
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Market Architecture
Securities also trade in a hybrid environment of market designs or architectures.
NYSE: Floor based auction organized by a specialist
Nasdaq: Interdealer electronic network
ECNs (ATS): Electronic networks with no dealer intermediaries (e.g. Archipelago, Instinet for
equities, BrokerTec, eSpeed for U.S. Treasuries)
Open outcry: CME, CBOT futures pits, Treasury phone based market
This is a rich area for empirical industrial organization research.
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Fundamental Concepts - Liquidity
Hasbrouck: “Liquidity is a summary quality or attribute of a security or asset market.
• Spreads: Difference between buyer and seller initiated prices.
• Depth: The quantity available for sale or purchase away from the current market price.
• Breadth: The market has many participants.
• Resilience: Price impacts caused by the trading are small and quickly die out.
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Fundamental Concepts – Price Discovery
Madhavan (2002, FAJ): Price discovery is the process by which prices incorporate new
information.
In the papers discussed today, we focus on the dimension of which market leads other markets in
the price discovery process. This concept is called information share. This paper is the first to
compare spot and derivatives markets in Treasuries.
Hasbrouck (1995): “The information share associated with a particular market is defined as the
proportional contribution of that market's innovations to the innovation in the common efficient
price.” Lehmann (2002): “a decomposition of the variance of innovations to the long run price.”
Enough chatting, let’s get to the model!!
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II. Hasbrouck Model
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Multimarket Security Model - Hasbrouck (1995)
p i,t  i p t i,t .
The price in security market i differs from the fundamental price p* only
transiently. The coefficient β is there because futures and cash markets may have a
slightly different basis.
p t p t1 t .
The fundamental price itself follows a random walk. Error terms ξ and η can be
contemporaneously and serially correlated.
This is called an unobserved components model because we don’t observe the
efficient price directly.
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Permanent Component
If we assume the individual prices are I(1), have a VAR(r) representation, and that
markets are cointegrated, the price vector has the Engle-Granger error correction form:
p t  zt1 A1 p t1  Ar p tr1 
t,
p 1,t1 2,t1 p 2,t1
z t1 

N11

.
Note daily coefficient to
adjust for futures basis
and/or cheapest to deliver.
p 1,t1 N,t1 p N,t1
Matrix of long run multipliers
1  N

1 

 


,
1  N
Note that this approach uses the reduced form VAR rather than the structural model (as
in Korenok, Mizrach and Radchenko (2006)).
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Effect of Shocks In the Long Run
In computing the long-run effects of a shock, we need to take into account
contemporaneous correlation

 E

t
t
by taking a Choleski decomposition, finding
M  i1  j1 mij such that MM 
N
i
Now, of course, we have all the same problems that the macroeconomists do. The
Choleski decomposition is not unique. (1) This paper reports upper bound estimates;
(2) An argument in favor of working directly with the structural model.
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Information Shares
Hasbrouck
Hj 
n
i m ij
 i
j

n
i m i1
i1
2


2
n
i m i2
i2
2
.



n m nn 
2
Gonzalo-Granger (Harris, McInish and Wood (2002))
GGj 
j

N
i
i1
.
Other estimates include deJong and Schotman (2004), Yan and Zivot
(2005). Lehmann (2002) attempts to reconcile these.
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III. Description of Treasury Market
Data and Architecture
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U.S. Treasury Market
Stage
Factoid
How Traded
Database
When Issued
Before auction
IDBs in
GovPX
ECN,
Voice
This
paper
eSpeed,
GovPX, BrokerTec
New Issues
Discrete issues
Auction
Treasury Department
On The Run
Commoditized
ECNs
Off the Run
Illiquid
Voice
eSpeed, BrokerTec
Mizrach
and Neely (2006)
Fleming, Mizrach, Neely
GovPX, BrokerTec
(2006)
Futures
Liquid
Trading pits CBOT,
CME
Trading pits CME,
CBOT
Futures
Cisco futures, TickData
This paper
This paper focuses on IDB voice transactions in GovPX, and futures market transactions
from the CME and CBOT,
Cantor’s eSpeed is discussed in Mizrach and Neely (2006). BrokerTec and
eSpeed are discussed in Fleming, Mizrach and Neely (2006).
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On-The Run Treasury Market in 2005
Mizrach/Neely (2006): On-the-run volume nearly 100% electronic, split between eSpeed and
BrokerTec, two ECNs.
39%
BrokerTec
eSpeed
61%
Momentum is with BrokerTec. Cantor had 70% share in 2001.
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Mizrach/Neely (2005) - Sample
Spot Market
2-year, 5-year and 10-year on the run Treasuries, October 1, 1995 to March 31, 2001,
from GovPX. Open outcry trades from 3 major firms: Garban-Intercapital; Hilliard
Farber; and Tullett Liberty.
A voice market with screen based display of quotes. This is NOT an ECN.
Trading is by large institutions. > 1mn$ per trade
Traders have a workup facility. Boni and Leach (2002).
Futures Market
3-month Eurodollar, 10-year and 30-year Treasury futures. The bonds trade on the
CBOT, the Eurodollar on the CME.
We use the hours that the futures markets are open: 8:20 to 15:00 CST.
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Trading Activity – Spot Markets
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Trading Activity on ECNs
2Y
5Y
10Y
30Y
eSpeed Btec
eSpeed
Btec
eSpeed
Btec
eSpeed Btec
851.88 1,805.92 2,220.32 3,644.49 2,541.09 3,913.39
748.34
612.42
Trades
Volume 7,491.16 18,053.63 10,946.95 15,262.98 11,163.86 13,400.46 1,911.66 1,258.02
Notes: The data are daily averages for the period October 12 - December 31, 2004.
Mizrach and Neely (2006) show a 635% growth in the 2-year and a 2000%
increase in the 5-year just on the eSpeed platform.
The Treasury market has joined the equity and foreign exchange markets in attracting
computerized trading. More than 50^ of BrokerTec quotes are from computerized
trades (Safarik, 2005) rather than from the primary dealers.
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Trading Activity – Futures Markets
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Liquidity – IDB Spot Market
Maturity
Two-year note
Five-year note
Ten-year note
Bid/Ask Spread (bp)
0.35
0.29
0.33
Depth (mn$) Market Impact (bp)
24.5
0.4235
10.7
0.9368
7.9
0.9066
Depth and spreads: Fleming (EPR, 2003). Sample period 1997-2000.
Market impact: Mizrach and Neely (2006) for January 1999.
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ECN Liquidity
Liq. Measure
Ticks
Inside Spr (bp)
Inside Bid
Depth
Inside Ask
Depth
Inside #Bids
Inside #Asks
Market Impact
2Y
Cantor ICAP
26,934 60,152
0.091
0.081
5Y
Cantor ICAP
70,105 113,887
0.103
0.090
10Y
Cantor ICAP
57,036 127,138
0.187
0.166
30Y
Cantor ICAP
68,621 54,308
0.296
0.328
71.77
105.48
26.69
28.65
35.31
33.27
8.57
6.72
71.55
8.39
8.24
0.0691
99.38
7.60
7.25
0.0361
26.16
6.07
6.07
0.2192
28.51
5.28
5.64
0.1054
34.96
8.20
8.15
0.0924
33.28
7.44
7.47
0.1611
8.76
3.55
3.64
0.7211
6.61
2.42
2.41
0.9416
Spreads and market impact have fallen by 75%.
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IV. Information Shares
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Comparison of Information Shares
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Summary of Information Share Results
These are upper bound estimates (placing spot market first in orthogonalization) of
bimarket comparisons between spot and the closest maturity future.
The spot markets all have information shares below 50% by 2001, with the decline
really accelerating after 1999. Futures vital to price discovery process.
H and GG measures have very similar qualitative results.
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Information Share in ECNs
1.00
0.90
0.80
0.70
Share
0.60
0.50
0.40
0.30
0.20
HMW
Havg.
0.10
28-Dec-04
21-Dec-04
14-Dec-04
7-Dec-04
30-Nov-04
23-Nov-04
16-Nov-04
9-Nov-04
2-Nov-04
26-Oct-04
19-Oct-04
12-Oct-04
0.00
BrokerTec 10-year information share. BTec > 50% except for 30-year.
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What Explains Information Share
We model IS share as a function of:
ln
IS 1,t c b 1 ln
N 1,t /
N 1,t N 2,t 
b 2 ln
S 1,t /
S 1,t S 2,t 
b 3 Di,t .
Where N1 is the number of trades in market 1, S1 is the percentage spread in market 1,
and Di is a dummy variable for 8 different sets of macroeconomic announcements.
Trades and spreads explain 10-15% of the differences in information shares. (Not bad
for microstructure studies).
Macroeconomic announcements are rarely significant. Only the employment
report (on 2 occasions) is significant.
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Full System Estimation
The GG story is a little cleaner: by 2001, the 10-year and 30-year futures have the dominant
information shares.
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V. Bayesian Structural Estimation
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State Space Representation
p t Hx t
x t Fx t1 v t ,
For the HUC model:
H
F
1
0 1N
0 N1 0 NN
, vt 
 I NN
, xt 
1 0 1N

t
 I NN
et
p t
ut
,
E
v
t v t 
2
2
2 2
We are interested in estimation of the structural parameters α, σ², Ω. Parameters are
estimated by MCMC, drawing the variance-covariance matrix of vt and computing α, σ²
and Ω using this matrix.
We also obtain confidence measures on these estimates from the Markov chain Monte
Carlo iterations. These are much less ad hoc than sample averages of daily estimates
and/or the upper lower bound estimates from the Hasbrouck orthogonalization.
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Information Shares – Mapping From Structural Model
Structural autocovariances:
Reduced form:
2
0 E
p t p 
2,

t  
1 E
p t p 
2.
t1 
p t  t Ct1 ,
#
#
C 
I 

.
0 CC , #
1 C.
#
Moments matched:
  
2,
Var

t   

Cov
p t , 
  2,
t 
Cov
p t , p t1 
I 
2.
#
#
#
Solution:
IS derived from these:
Mizrach/Neely Tatonnement
 2
, #
 1 2.
#
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Information Shares
All of the information share measures in the literature: (1) Hasbrouck (H); (2)
Gonzalo-Granger (GG); (3) deJong and Schotman (DS); (4) Yan and Zivot
(YZ) can be redefined in terms of parameters of the structural model.
We also obtain confidence measures on these estimates from the Markov chain Monte
Carlo iterations. These are much less ad hoc than sample averages of daily estimates
and/or the upper lower bound estimates from the Hasbrouck orthogonalization.
Caveats:
(1) GG Information shares can be negative.
(2) Hasbrouck shares are positive by construction, but can give the largest IS to a
market which moves prices away from the efficient price.
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Conclusion
Information shares are a useful summary statistic of the relative importance of market
structures that are fragmented.
Despite strong identification assumptions, these measures correlate well with
observable liquidity.
Treasury market: Ignore the futures at your peril. ECNs and black boxes
makes this market potentially more volatile. Supply constraints
(reopenings, off-the-run, close substitutes like corporates) make these
concerns second order in my view.
Direct estimation of the structural model seems to be the best way to go forward
in this literature.
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