1 2006 national taiwan university international conference in finance michael t. chng dept of...
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2006 National Taiwan UniversityInternational Conference in Finance
Michael T. ChngDept of Finance, University of Melbourne
Aihua XiaDept of Mathematics & Statistics, University of
Melbourne
The price formation of substitute markets
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Introduction• Price discovery: the process by which private information
implicit in investor trading is revealed in subsequent price formation.
• Price formation models:• Hasbrouck (1991a,b): Signed trade size• Madhavan, Richardson and Roomans (1997): trade
direction • Dufour and Engle (2000): time between trades• Al-Suhaibani and Kryzanowski (2000): order size• Chng (2005): trade and net order sizes.
• All of the above are single market models, although some models consider two or more trading parameters.
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Literature review• J. Financial Markets dedicated a special issue [5(3), 2002]
to the two commonly used measures of cross market price discovery:
• Gonzalo & Granger (1995) common factor weights (JBES):• Computes the coefficient of error correction terms to infer
orthogonal weights on the efficient price contributed by various price sequences.
• Hasbrouck (1995) information share (JF):• Computes contribution to the variance of the efficient price
change by various price sequences.
• Both consider only price parameters of multiple markets.
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Main objectives• Derive a joint trade direction model (JTDM) from the
single market MRR (1997) trade direction model.
• Demonstrate the use of the JTDM and test it against the VECM using a comprehensive sample of 20 Chinese twin-board firms (A-B & A-H)
• Lee and Rui (2000), Sun and Tong (2000), Wang and Jiang (2004) and Yeh, Lee and Pen (2004) use a sample period that is prior to either or both:
• Feb 2001: Locals with forex accounts can trade B-shares • Dec 2002: QFII are allowed to trade A-shares
• This becomes a test of the relevance of price versus non-price parameter in cross market price formation.
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The MRR (1997) model• Highlights the role of 1st order serial
correlation in trade direction Xt-1 • Xt assumed to follow a general Markov
process
• The model considers 3 states S: {+1, 0, -1}
• 3x3 transition matrix
• Transition of Xt illustrated in Figure 1
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The MRR (1997) model
1 1
1 1
( [ | ])
* [ | ] , where 2 (1 )
[ (1 ) (1 ) ]
t t t t t t
t t t t
t t t
t t t t t
u u X E X X
p u X
E X X X
r p L L X
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Our model• A bivariate system that highlights the joint
trade direction (Xt, Yt) in price formation.
• (Xt, Yt) assumed to follow a general Markov process
• We consider 4 states S:{(1,1), (1,-1), (-1,1), (-1,-1)}
• 4x4 transition matrix
• Transition of (Xt, Yt) illustrated in Figure 2
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Categorizing the 16 transitions
• Full continuation: Pr (Xt=Xt-1,Yt=Yt-1|Xt-1 ,Yt-1) =
• X-continuation: Pr (Xt=Xt-1,Yt=-Yt-1|Xt-1 ,Yt-1) = X
• Y-continuation: Pr (Xt=-Xt-1,Yt=Yt-1|Xt-1 ,Yt-1) = Y
• Full reversal: Pr (Xt=-Xt-1,Yt=-Yt-1|Xt-1 ,Yt-1) = (1--X-Y)
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The model’s focus• To infer Pr (X-continuation) = X
Pr (Y-continuation) = Y
• Conditional on opposite trade directions observed at t-1, the JTDM measures which market is more likely to persist in the same direction i.e. continuity.
• This has a natural interpretation as a measure of price leadership/discovery.
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Bivariate structural system1 1 1
1 1 1 1
1 1
( [ | , ])
* [ | , ]) ,
where 2( ) 1 and 2( ) 1
2( )
( ) (1 ) (1 )
(
t t t t t t t t t
X X Xt t t t
Y Y Yt t t t
t t t t t t
X Y
X Y
X X Xt t t t t t
Yt
u u X Y E X Y X Y
p u X
p u Y
E X Y X Y X Y
r X Y X Y
r
1 1) (1 ) (1 )Y Yt t t t tY X X Y
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Twin-share Chinese firms• Why Chinese market?
• Chinese financial markets attracting increasing attention• Multiple exchanges (SHSE, SZSE HKEx) and multiple listing boards
(A, B, H)• Similar institutional characteristics• Large number of twin-board firms; overlapping trading hours.
• Some institutional details• SHSE: A-shares in RMB; B-shares in USD• SZSE: A-shares in RMB; B-shares in HKD• HKEx: H-shares in HKD• A, B, H, A-B or A-H, but not B-H.
• Either the B or H board provides access to a substantial foreign investor clientele, although they are not foreign boards per se.
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Sampling methodology
• For all firms that are selected:• Tradable share ≥ 30% of issued capital
(2005 overall average)
• Must have ≥ 10% of issued capital allocated to each board.
• Tradable capital on the smaller board is ≥ 1/5 that which is issued on the larger board.
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Overall sample• A pair of A-B and A-H firms for each of 10 sectors
of the Chinese economy.
• Sample period: 4th Jan~30th Sep 05 ( 170 days).
• Each day has 100 min-by-min trade observations.• All 3 exchanges host a morning and afternoon session • Restrict to overlapping trading hours on both sessions• 10:05-11:24; 14:35-14:54
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Testing methodology• Apply GMM procedure on the bivariate
system to estimate the 5 trading parameters.
• Specify 6 moment conditions
E
X X X
Y Y Y
X
Y
X
Y
t t t
t t t
tX
t
tXt
tY
t
tYt
F
H
GGGGGGGG
I
K
JJJJJJJJ
1 12
1 12
1
1
1
1
0
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Testing methodology• Apply VECM & JTDM to rank twin boards
for each of 20 firms.
• When models give conflicting rankings, apply Wald test and J-test statistics to model selection.
• Either or both tests favour one model over the other
• Both test statistics are conflicting or fail to reject both models.
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Main results• VECM and JTDM give consistent ranking in 6 firms; 3
firms (Southern Airline, China Shipping and ZTE Corp) provide strong evidence of H-board performing price discovery.
• Wald and J tests indicate VECM (JTDM) as the preferred model for 3 firms. In all 3, the B/H (A) board is ranked above the A (B/H) board.
• JTDM ranks A above B/H for the 3 firms with the highest % of no-trade in their B/H samples.
• VECM and JTDM generate conflicting rankings in 8 out of 10 A-B firms. Subsequent Wald and J tests fail to reject both models in 7 of those 8 firms.
• Unable pick up distinctions in trading since the boards themselves are no longer distinct.
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The informativeness ofcorporate bond trades
ByPeter Chen, Junbo Wang & Chunchi Wu
Discussant’s report by Michael T. Chng
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Background
• Empirical (daily & intraday) analysis of the contribution of trades to price discovery in the US corporate bond market.
• Report six sets of results:• OLS (1 & 2-step regression)• VAR (bivariate and bivariate with
duration)• GARCH (univariate and bivariate)
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Motivation• Lack of study on volume-volatility dynamics of corporate
bond market.
• Reliable transaction data not readily available until recent years.
• 3 measure of trading activity:• daily volume• trade size• number of trades
• Contrary to equity studies, trading activity does not play a significant role in volatility dynamics.
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Comments• This is a detailed empirical analysis.
• The results are well presented & well discussed.• Important as there are a lot of results to churn through
• I believe it is at least a 2nd draft, and may be in a later stage of journal review.
• The main question I ponder on is the need to go through six empirical analysis. I have 3 reasons for making this comment.
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1st reason• The bond market is a clear underdog.
• The authors report that daily bond trade averages 0.53% of corresponding daily stock trades.
• For the market to learn from trading activity, there must be enough generated parameters to begin with.
• The paper contributes by providing formal empirical evidence.
• It is the value of their numerous robustness checks that I query.
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2nd reason• Second, even if I accept that 6 sets of
results are necessary, I would actually view them as 3 pairs of alternative empirical estimation.
• For each pair, surely one specification is more appropriate than the other.
• E.g. If bivariate GARCH is appropriate, why consider univariate GARCH at all?
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3rd reason• There is a need to distinguish between the
informational efficiency of the US corporate bond market & the informativeness of US corporate bond trades.
• If bond trade parameters are found to be informative, this suggest that the bond market is (more or less) performing price discovery.
• But if bond trade parameters are not found to be informative, this does not imply that that US corporate bond market is NOT performing price discovery.
• Quotes could still adjust in the absence of trading, and in response to non-trade parameters.
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Suggestions• Rather than presenting 3 sets of ‘overlapping’
results, maybe the authors could consider reducing the set of results and instead:
• Providing more institutional details to further motivate a study on bond markets and potential causes for trades to be non-informative, and/or
• Consider other intraday measures of trade informativeness often used in microstructure studies:
• Hasbrouck family of measures (1991a, 1991b, 1993): signed trade size
• Madhavan, Richardson and Roomans (1997) : trade direction
• Theobald and Yallup (2004): speed of adjustment coefficients
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Questions• Why is the stock-bond transmission effect examined in a
bivariate GARCH and not as a 4-equation VAR?
• Price discovery in equity markets is caused by interaction among distinct investor clienteles (retail/institutional; local/foreign; liquidity/informed). Do the sample clienteles readily apply to the corporate bond market?
• Is it necessarily true that debt and equity securities similarly reflect the value of a firms assets?
• Should the authors perform a nested test on Eq (2)~(4) since (2) and (3) are nested in (4)? Similar for Eq (5)~(7).
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Editorial• The paper is well-written, but maybe it has too
many equation numbers.
• Maybe Eq (2), (3) & (4) can be presented as one equation since (2) and (3) are nested in (4)?
• Similar comment for Eq (5), (6) & (7)
• Eq (8) & (9) is a bivariate system and should be labeled under as one equation number.
• Similar comment for Eq (13) & (14)
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Time varying GARCH and nested causality relations between intraday return and
order imbalance in Nasdaq-100 component stocks
ByYong Chern Su & Han Ching Huang
Discussant’s report by Michael T. Chng
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Background This paper analyze the role of order
imbalance (OI) on return and return volatility dynamics in a GARCH framework for Nasdaq 100 component stocks.
OI is defined as buyer minus seller initiated trades OINUM: number of trade OISHA: Number of shares OIDOL: Dollar terms
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Data
For each of 100 stocks: Sample period: Month of Dec 2003 Each trading day partition into 3 sub-
periods: 9:30-11:30 11:30-14:30 14:30-16:00
Sample frequency is 90-sec
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Comments I think the authors did well in handling such a
comprehensive database.
They trade off time-series robustness for cross-sectional robustness.
However, I am sure a potential referee would still question how representative are time-series results based solely on Dec data.
Hence authors should highlight details of previous slide.
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Comments
The 5-sec rule in Lee and Ready (1991) applies to NYSE and AMEX tick data. Not sure how relevant it is to Nasdaq
data.
Is it possible to provide a reference that applies the 5-sec rule on Nasdaq data?
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Comments Authors present two sets of GARCH (1,1)
results with slightly different specifications to both mean and variance equations.
Eq (1)~(2) versus Eq (5)~(6)
As OIit-1 can be negative, could there be problem applying Eq (2) out of sample?
I guess this makes Eq (5)~(6) appealing.
If this is the case, shouldn’t one GARCH specification suffice for empirical estimation.
Could vest excess effort to expand sample period.
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Comments Authors motivate their choice of 3 proxy
variable for information asymmetry across firms.
However, I think that the analysis itself is not well motivated. Why should the return-order imbalance
relation vary with the degree of information asymmetry?
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Suggestions I got confused reading from Eq (7) to Eq
(8) to Eq (9). From Eq (7) to Eq (8):
Shifting the dynamics back 1 period to focus on out-of-sample predictive ability of OI on return generating process.
From Eq (8) to Eq (9): Wouldn’t it be more interesting to
investigate cross-sectional discrepancies in the relevance of OI in return based on varying degrees of information asymmetry.
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Editorial This paper attempts to cover quite a
few issues. Maybe the authors could write an objective paragraph on page 1 listing their (4?) objectives. GARCH (1,1) estimation of return, volatility
and OI dynamics Contrast OI & trading volume How the return and OI interaction vary
across information asymmetry Causality tests between return and OI in a
VAR framework.
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Editorial Should the various coefficients from
Eq (1)~(7) have a subscript i since it is written against Rit?
Footnote 5: “event day” ??
Abstract & title both quite lengthy
Chordia, Roll and Subrahmanyam (2005) in the JFE is a good ref to include.
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Life cycle of the weekend effect
ByNan Ting Chou
Charles Mossman &Dennis Olson
Discussant’s report by Michael T. Chng
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Background
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14th Securities and Financial Markets (SFM) conference, Kaohsiung
Michael T. ChngDept of Finance, University of Melbourne
Aihua XiaDept of Mathematics & Statistics, University of
Melbourne
The price formation of substitute markets