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2006 National Taiwan University International Conference in Finance

2006 National Taiwan University International 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. Introduction.

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2006 National Taiwan University International Conference in Finance

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  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. The MRR (1997) model

  7. 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

  8. 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)

  9. 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.

  10. Bivariate structural system

  11. 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.

  12. 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.

  13. 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

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

  15. 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.

  16. 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.

  17. The informativeness ofcorporate bond trades By Peter Chen, Junbo Wang & Chunchi Wu Discussant’s report by Michael T. Chng

  18. 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)

  19. 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.

  20. 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.

  21. 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.

  22. 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?

  23. 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.

  24. 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

  25. 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).

  26. 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)

  27. 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 Discussant’s report by Michael T. Chng

  28. 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

  29. 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

  30. 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.

  31. 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?

  32. 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.

  33. 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?

  34. 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.

  35. 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.

  36. 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.

  37. Life cycle of the weekend effect By Nan Ting Chou Charles Mossman & Dennis Olson Discussant’s report by Michael T. Chng

  38. Background

  39. 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

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