2006 National Taiwan University International Conference in Finance

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2006 National Taiwan UniversityInternational
Conference in Finance
The price formation of substitute markets

• Michael T. Chng
• Dept of Finance, University of Melbourne
• Aihua Xia
• Dept of Mathematics Statistics, University of
  Melbourne

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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

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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 (XtXt-1,YtYt-1Xt-1
  ,Yt-1) ?
• X-continuation Pr (XtXt-1,Yt-Yt-1Xt-1 ,Yt-1)
  ?X
• Y-continuation Pr (Xt-Xt-1,YtYt-1Xt-1 ,Yt-1)
  ?Y
• Full reversal Pr (Xt-Xt-1,Yt-Yt-1Xt-1 ,Yt-1)
  (1-?-?X-?Y)

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The models 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 system
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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 Jan30th 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
• 1005-1124 1435-1454

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Testing methodology

• Apply GMM procedure on the bivariate system to
  estimate the 5 trading parameters.
• Specify 6 moment conditions

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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

• By
• Peter Chen, Junbo Wang Chunchi Wu
• Discussants 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

• By
• Yong Chern Su Han Ching Huang
• Discussants 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
• 930-1130
• 1130-1430
• 1430-1600
• 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, shouldnt 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)
• Wouldnt 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

• By
• Nan Ting Chou
• Charles Mossman
• Dennis Olson
• Discussants report by Michael T. Chng

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Background
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14th Securities and Financial Markets (SFM)
conference, Kaohsiung
The price formation of substitute markets

• Michael T. Chng
• Dept of Finance, University of Melbourne
• Aihua Xia
• Dept of Mathematics Statistics, University of
  Melbourne