This paper examines the process of price discovery in the MTS system, which builds on the parallel quoting of euro-denominated government securities on a number of (relatively large) domestic markets and on a (relatively small) European marketplace EuroMTS). Using twenty-seven months of daily
data for 107 pairs of bonds, we present unambiguous evidence that trades on EuroMTS have a sizeable informational conten

Caporale, GM

Girardi, A

English

Brunel University Research Archive

Department of  Economics and Finance                    Working Paper No. 09-41  http://www.brunel.ac.uk/about/acad/sss/depts/economics Economics and Finance Working Paper Series Guglielmo Maria Caporale and Alessandro Girardi   Price formation on the EuroMTS Platform  December 2009 Price formation on the EuroMTS Platform Guglielmo Maria Caporale Brunel University, London  Alessandro Girardi Institute for Economic Studies and Analyses, Rome  December 2009  Abstract This paper examines the process of price discovery in the MTS system, which builds on the parallel quoting of euro-denominated government securities on a number of (relatively large) domestic markets and on a (relatively small) European marketplace (EuroMTS). Using twenty-seven months of daily data for 107 pairs of bonds, we present unambiguous evidence that trades on EuroMTS have a sizeable informational content.   Keywords: MTS system, price discovery. JEL Classification: C32, G10.  Corresponding author: Professor Guglielmo Maria Caporale, Centre for Empirical Finance, Brunel University, West London, UB8 3PH, UK. Tel.: +44 (0)1895 266713. Fax: +44 (0)1895 269770. Email: Guglielmo-Maria.Caporale@brunel.ac.uk  [1]1 Introduction The recent availability of high-quality transaction data has led to a number of empirical studies aimed at shedding light on how European government bond markets work (see Menkveld et al., 2004; Cheung et al., 2005; Dunne et al., 2007; Beber et al., 2008, among others). The present paper contributes to this growing body of research by investigating the process of price discovery (i.e. the timely incorporation of information arrivals into market prices through trading), in the most relevant electronic platform for euro-denominated government bonds, i.e. the duplicated market setting of the MTS (Mercato Telematico dei Titoli di Stato) system, which builds on a number of domestic markets and a centralized European marketplace (EuroMTS). The extent to which the institutional architecture of the MTS system can create an efficient environment to trade Treasury securities is being debated in academic and policy circles. A number of observers subscribe to “the redundancy hypothesis” of Cheung et al. (2005) for a centralized European marketplace as bonds being traded on EuroMTS are a fraction of the portfolio of securities traded on the domestic MTS platforms. Given this criticism, this paper aims at quantifying the degree of price discovery on the EuroMTS market by using an original and extensive dataset of daily transaction prices for 107 euro-denominated government bonds over a 27-month horizon. 2 A duplicated market setting: E pluribus unum? The main electronic dealer-to-dealer platforms to trade euro-denominated Treasury securieties are MTS, Icap/BrokerTec Eurex Bonds and eSpeed, with the MTS system accounting for 40% of government bond transactions (Galati and Tsatsaronis, 2003)  [2]and 72% volume of electronic trading (Persaud, 2006). All government marketable bonds issued by euro area Member States are listed on their respective domestic MTS platforms. Only benchmark securities, or on-the-run bonds with an outstanding value of at least 5 billion euro and satisfying a number of listing requirements are admitted, instead, to trading on EuroMTS. For benchmark securities, thus, dealers are allowed to post their quotes on both market simultaneously (parallel quoting). As a background to the discussion, Figure 1 shows (the logarithm of) daily transaction prices of a benchmark bond, over the period January 2004 - March 2006.  [Figure 1] As can be seen, the series overlap very closely. This is not surprising since the prices of the same bond recorded in multiple markets are not independent of one another. The process of price formation, however, may occur entirely in one market or, more typically, may be split among marketplaces. As benchmark bond trading takes place for the most part in the domestic MTS markets (Cheung et al., 2005), the informational content of prices recorded on the EuroMTS is doubtful. In the MTS system, indeed, EuroMTS seems to be a prototype of a “satellite market” (in the sense of Hasbrouck, 1995), competing with a number of large domestic markets. 3 Econometric framework Consider a bond traded on EuroMTS ( E ) and on its domestic MTS market ( D ). Its (log-) price in market ,j E D=  at time t , jtp , can be represented as the sum of a common permanent component (capturing information arrivals cumulating over time), tφ , and an idiosyncratic transitory part (capturing market-specific  [3]characteristics), jtυ :  j jt t tp = φ + υ  (1) The law of motion of the permanent part is 1 01tt t t iiφ φ−=φ = φ +μ = φ + μ∑ , with initial conditions 0φ  and tφμ  such that ( ) 0tE φμ = , 2 2( )tE φ φμ = σ , ( ) 0t sE φ φμ μ =  for s t≠ . The jtυ  term is a covariance stationary process 1( )j j j j jt i t i tiL∞−=υ = δ ξ = δ ξ∑ , where jtξ ’s are independently distributed with mean zero and constant variance. Under these assumptions, the two log-price series, albeit individually non-stationary, are linked to one another by a stationary equilibrium condition:  ( ) ( )E D E E D Dt t t t tp p L L− = δ ξ −δ ξ = ε  (2) The empirical implications of equation (2) can be suitably captured by specifying, for each pair ( Etp ,Dtp ), a Vector Error Correction model (Johansen, 1995), which constitutes the basis to construct price discovery statistics as suggested by Harris et al. (1995) and Hasbrouck (1995):  1111EE E Ekt jt t tj DD D Dj t jt t tpp p uApp p u− −−= −−⎡ ⎤Δ⎡ ⎤ ⎡ ⎤ ⎡ ⎤Δ = Π ⋅ + ⋅ +⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ΔΔ ⎢ ⎥⎣ ⎦ ⎣ ⎦ ⎣ ⎦⎣ ⎦∑ , ( )22E E Dt tE D DE u u⎡ ⎤σ ρσ σ′⋅ = Σ = ⎢ ⎥ρσ σ σ⎣ ⎦ (3) where Δ  is the first difference operator, A ’s are matrices of autoregressive coefficients, u ’s are residuals, ρ  is the correlation coefficient and σ ’s are standard deviations. If condition (2) holds, the long-run matrix Π  can be factored as:   [ ]1 1ED⎡ ⎤αΠ = ⋅ −⎢ ⎥α⎣ ⎦  (4)  [4]with feedback parameters such that 0Eα <  and 0Dα > . Harris et al. (1995) attribute superior price discovery to the market that adjusts the least to price movements in the other market:   DE D Eαγ = α −α , ED E Dαγ = α −α  (5) so that EuroMTS (domestic MTS) market’s contribution to price discovery, Eγ  ( Dγ ), depends on both α ’s. Hasbrouck’s model defines markets’ contribution to price discovery as their contribution in explaining the variance of the innovations to the common factor. With price innovations correlated across markets, Hasbrouck’s approach can only provide upper and lower bounds. Using condition (5), they can be written for the EuroMTS market as: 22 2 2 2( )( ) (1 )ub E E D DEE E D D D DS γ σ +ργ σ= γ σ +ργ σ + γ σ −ρ  , 2 2 22 2 2 2(1 )(1 ) ( )lb E EEE E E E D DS γ σ −ρ= γ σ −ρ + ργ σ + γ σ  respectively. However, Baillie et al. (2002) argue that the average of the bounds:  1 ( )2ub lbE E ES Sζ = +  (6) provides a sensible estimate of markets’ contribution in determining the efficient price. Both Eγ  and Eζ  can range in the [0,1] interval, with 1E D E Dγ + γ = ζ + ζ = . High (low) values of the statistics indicate sizeable EuroMTS (domestic MTS) market’s contribution to price discovery.1  4 Empirical results 4.1 Data and preliminary analyses                                                   1  See Ballie et al. (2002) for a detailed discussion of the two price discovery measures.  [5]Daily data over the period 02/01/2004 to 31/03/2006 for the last transaction prices (reference prices) recorded before market close are extracted from the MTS Time series database. All euro-denominated government securities traded in January 2004 maturing after the end of our estimation horizon are included: a total of 107 bonds, whose codes are listed below. [Table 1] The estimation horizon ranges from 557 to 585 observations, with an average of 580 datapoints.2 Standard ADF test results for each of 214 individual log-price series lead to reject the null hypothesis of a unit root at conventional levels of significance. On the other hand, differencing the series appears to induce stationarity.3 The trace test suggests choosing rank 1 for Π  in 104 models.4 The symmetry and proportionality assumption implied by condition (2) is tested through a standard 2χ -distributed LR test. In 94 models, the over-identifying restriction is not rejected by the data at (least at) the 5% level of significance. For the remaining 10 cases, the evidence is less conclusive, even though the existence of a [1 1]′−  cointegration vector is strongly supported by the Horvath and Watson (1995) test. As for the feedback parameters, both α ’s are correctly signed, implying direct convergence towards the long-run relationship in all but six models. 4.2 The EuroMTS market’s contribution to price discovery Discarding the cases with wrongly signed α ’s, Figure 2 presents the scatter plot ( Eγ                                                    2 Following Upper and Werner (2002), in the case of missing observations (owing to lack of transactions) we use the last available transaction price (“fill-in” method). 3 Complete results of this Section are available upon request. 4 In three models, the rank of Π  turns out to be two, which is not consistent with the conclusions from the unit root tests but confirms that condition (2) holds in these cases too.  [6]versus Eζ ) of price discovery measures for the (107-3-6=98) remaining models. [Figure 2] Even though estimated values for Eγ  and Eζ  reveal that the process of price discovery takes place mainly in the domestic markets for all but two models (those in quadrant I), their averages values (roughly 0.2) are significantly different from zero, according to asymptotic and a number of bootstrap (with 1000 replicates) 95% confidence intervals (Table 2, Panel A).5 Moreover, when testing for the equivalence of the mean ( Eγ  minus Eζ ) the null cannot be rejected, suggesting that considering Eγ  or Eζ  leads to the same conclusions, as also confirmed by their strong correlation (0.81).6 Finally, with wrongly signed Dα ’s replaced by zero (as in Blanco et al., 2005), the two price discovery measures in the larger sample of 107-3=104 models (Table 2, Panel B) are highly correlated, with their average values not statistically different and quite close in magnitude to their counteparts in Panel A.7 [Table 2]                                                   5 Although asymptotic intervals are not very sensitive to the assumption of normality, QQ-plots and normality tests indicate clear departures from this assumption for Eγ  and Eζ  in the two samples.  6 The “fill-in” method may influence the short-term information flow for the less frequent trading marketplace (EuroMTS, in the present case) even if the trades taking place on that market do contain information (Lehmann, 2002). Accordingly, our estimated values for Eγ  and Eζ  can be considered as lower bounds. 7 By comparing the mean value of Eγ  ( Eζ ) in Panel A to its counterpart in Panel B, the null of equivalence is not rejected according to asymptotic and bootstrap-based tests. Furthermore, replicating these computations for weighted quantities by traded volumes or by average number of trades over the 27-month horizon considered gives quantitatively similar results (not reported) to those in Table 2.  [7]5 Conclusions This paper documents that the duplicated market setting of the MTS system is able to eliminate persistent price discrepancies for the same bond traded on the domestic MTS and the EuroMTS platforms, with about 20% of price discovery occurring in the European marketplace. Our results clearly suggest that trades on EuroMTS have a sizeable informational content, in contrast to the “redundancy hypothesis”. It is widely recognized that markets’ contribution to price discovery may be influenced by market-specific characteristics as well as by institutional arrangements. Addressing this issue is of relevance for policy makers, as the degree of price discovery might be entirely due to liquidity conditions, institutional features or possibly both, with different implications for developing a more efficient regulatory framework. This topic is left for future research. References Baillie R.T., G.G. Booth, Y. Tse, T. Zabotina (2002), Price Discovery and Common Factor Models, Journal of Financial Markets, 5, 309-321. Beber A., M.W. Brandt, K.A. Kavajecz (2008), Flight-to-Quality or Flight-to-Liquidity? Evidence from the Euro-Area Bond Market, Review of Financial Studies, forthcoming.  Blanco R., S. Brennan, I. Marsh (2005), An Empirical Analysis of the Dynamic Relation between Investment-Grade Bonds and Credit Default Swaps, Journal of Finance, 60, 2255-2281. Chakravarty S., H. Gulen, S. Mayhew (2004), Informed Trading in Stock and Option Markets, Journal of Finance, 59, 1235-1258. Cheung C., F. de Jong, B. Rindi (2005), Trading European Sovereign Bonds: The Microstructure of the MTS Trading Platforms, ECB Working Paper, 432. Dunne P.G., M.J. Moore, R. Portes (2007), Benchmark Status in Fixed-Income Asset Markets, Journal of Business Finance and Accounting, 34, 1615-1634.  [8]Galati G., K. Tsatsaronis (2003), The Impact of the Euro on Europe’s Financial Markets, Financial Markets, Institutions and Instruments, 12, 165-221. Harris F., T. McInish, G. Shoesmith, R. Wood (1995), Cointegration, Error Correction, and Price Discovery on Informationally Linked Security Markets, Journal of Financial and Quantitative Analysis, 30, 563-579. Hasbrouck J. (1995), One Security, Many Markets: Determining the Contributions to Price Discovery, Journal of Finance, 50, 1175-1199.  Horvath M.T.K., M.W. Watson (1995), Testing for Cointegration when Some of the Cointegrating Vectors Are Prespecified, Econometric Theory, 11, 984-1014. Johansen S. (1995), Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, Oxford University Press. Lehmann B.N. (2002), Some Desiderata for the Measurement of Price Discovery across Markets, Journal of Financial Markets, 5, 259-276. Menkveld A.J., Y.C. Cheung, F. de Jong (2004), Euro Area Sovereign Yield Dynamics: The Role of Order Imbalance, ECB Working Paper, 385. Persaud A.D. (2006), Improving Efficiency in the European Government Bond Market, Discussion Paper ICAP plc.  Upper C., T. Werner (2002), How Resilient Are Financial Markets to Stress? Bund Futures and Bonds during the 1998 Turbulence, BIS Papers, 12, 110-123.  [9]Table 1. Bond codes    [10]Table 2. Tests for the mean values of Eγ  and Eζ  γE ζE γE ζEMean 0.1966 0.2064 0.1853 0.2031Correlation (95% confidence interval)Asymptotic interval (0.1704 , 0.2228) (0.1832 , 0.2296) (0.1590 , 0.2115) (0.1807 , 0.2256)Bootstrap: normal approximation interval (0.1665 , 0.2152) (0.1870 , 0.2353) (0.1525 , 0.1993) (0.1882 , 0.2359)Bootstrap: percentile interval (0.1804 , 0.2253) (0.1782 , 0.2252) (0.1717 , 0.2182) (0.1714 , 0.2178)Bootstrap: adjusted percentile interval (0.1706 , 0.2170) (0.1875 , 0.2388) (0.1610 , 0.1994) (0.1883 , 0.2295)Bootstrap: studentized interval (0.1736 , 0.2252) (0.1835 , 0.2316) (0.1603 , 0.2131) (0.1801 , 0.2271)Asymptotic intervalBootstrap: normal approximation intervalBootstrap: percentile intervalBootstrap: adjusted percentile intervalBootstrap: studentized interval(-0.0449 , 0.0259)(-0.0452 , 0.0275)(-0.0522 , 0.0165)(-0.0522 , 0.0167) (-0.0537 , 0.0156)(-0.0547 , 0.0148)(-0.0526 , 0.0170)(-0.0446 , 0.0250)(-0.0443 , 0.0266) (-0.0470 , 0.0241)Panel A: 98 bonds Panel B: 104 bondsTest for the significance of the means (95% confidence intervals)Test for the equivalence of the means (95% confidence intervals)0.8116 (0.7309 , 0.8699) 0.7820 ( 0.6940 , 0.8471)  [11]Figure 1. Daily transaction prices (in logs) on the MTS system (bond code: IT0003242747) 4.604.654.704.754.8001/04 04/04 07/04 10/04 01/05 04/05 07/05 10/05 01/06Domestic MTS EuroMTS              [12]Figure 2. Scatter plot: Eγ  versus Eζ  0.00.10.20.30.40.50.60.70.80.91.00.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0γ Εζ Ε45° lineI II III IV   

An Empirical Analysis of the Dynamic Relation between Investment-Grade Bonds and Credit Default Swaps,

Benchmark Status in Fixed-Income Asset Markets,

Cointegration, Error Correction, and Price Discovery on Informationally Linked Security Markets,

Euro Area Sovereign Yield Dynamics: The Role of Order Imbalance,

Flight-to-Quality or Flight-to-Liquidity? Evidence from the Euro-Area Bond Market, Review of Financial Studies,

How Resilient Are Financial Markets to Stress? Bund Futures and Bonds during the

Informed Trading in Stock and Option Markets,

Likelihood-Based Inference in Cointegrated Vector Autoregressive Models,

One Security, Many Markets: Determining the Contributions to Price Discovery,

Price Discovery and Common Factor Models,

Rindi (2005), Trading European Sovereign Bonds:

Some Desiderata for the Measurement of Price Discovery across Markets,

Testing for Cointegration when Some of the Cointegrating Vectors Are Prespecified,

Price formation on the EuroMTS platform

Price formation on the EuroMTS platform

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