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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. Overview of Papers Discussed.

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Overview of Papers Discussed

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  1. Mizrach/Neely Tatonnement

  2. Bruce Mizrach and Chris Neely (2006), “The Microstructure of Bond Market Tatonnement,” St. Louis Federal Reserve Working Paper #2005-70a. Overview of Papers Discussed 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.” Mizrach/Neely Tatonnement

  3. Conceptual Material on Market Microstructure Structural Model Description of Treasury Market Data and Architecture Estimates of Treasury Market Information Alternative Bayesian Structural Approach Conclusion Outline Mizrach/Neely Tatonnement

  4. I. Market Microstructure Concepts Mizrach/Neely Tatonnement

  5. 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. Market Microstructure Mizrach/Neely Tatonnement

  6. Similar or identical securities often trade in multiple venues. Market Fragmentation 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. Mizrach/Neely Tatonnement

  7. 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. Market Architecture Mizrach/Neely Tatonnement

  8. Hasbrouck: “Liquidity is a summary quality or attribute of a security or asset market. Fundamental Concepts - Liquidity • 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. Mizrach/Neely Tatonnement

  9. 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.” Fundamental Concepts – Price Discovery Enough chatting, let’s get to the model!! Mizrach/Neely Tatonnement

  10. II. Hasbrouck Model Mizrach/Neely Tatonnement

  11. Multimarket Security Model - Hasbrouck (1995) 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. 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. Mizrach/Neely Tatonnement

  12. 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: Note daily coefficient to adjust for futures basis and/or cheapest to deliver. Matrix of long run multipliers Note that this approach uses the reduced form VAR rather than the structural model (as in Korenok, Mizrach and Radchenko (2006)). Mizrach/Neely Tatonnement

  13. Effect of Shocks In the Long Run In computing the long-run effects of a shock, we need to take into account contemporaneous correlation by taking a Choleski decomposition, finding 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. Mizrach/Neely Tatonnement

  14. Information Shares Hasbrouck Gonzalo-Granger (Harris, McInish and Wood (2002)) Other estimates include deJong and Schotman (2004), Yan and Zivot (2005). Lehmann (2002) attempts to reconcile these. Mizrach/Neely Tatonnement

  15. III. Description of Treasury Market Data and Architecture Mizrach/Neely Tatonnement

  16. U.S. Treasury Market 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). Mizrach/Neely Tatonnement

  17. On-The Run Treasury Market in 2005 Mizrach/Neely (2006): On-the-run volume nearly 100% electronic, split between eSpeed and BrokerTec, two ECNs. Momentum is with BrokerTec. Cantor had 70% share in 2001. Mizrach/Neely Tatonnement

  18. 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. Mizrach/Neely Tatonnement

  19. Trading Activity – Spot Markets Mizrach/Neely Tatonnement

  20. Trading Activity on ECNs 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. Mizrach/Neely Tatonnement

  21. Trading Activity – Futures Markets Mizrach/Neely Tatonnement

  22. Liquidity – IDB Spot Market Depth and spreads: Fleming (EPR, 2003). Sample period 1997-2000. Market impact: Mizrach and Neely (2006) for January 1999. Mizrach/Neely Tatonnement

  23. ECN Liquidity Spreads and market impact have fallen by 75%. Mizrach/Neely Tatonnement

  24. IV. Information Shares Mizrach/Neely Tatonnement

  25. Comparison of Information Shares Mizrach/Neely Tatonnement

  26. 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. Mizrach/Neely Tatonnement

  27. Information Share in ECNs BrokerTec 10-year information share. BTec > 50% except for 30-year. Mizrach/Neely Tatonnement

  28. What Explains Information Share We model IS share as a function of: 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. Mizrach/Neely Tatonnement

  29. Full System Estimation The GG story is a little cleaner: by 2001, the 10-year and 30-year futures have the dominant information shares. Mizrach/Neely Tatonnement

  30. V. Bayesian Structural Estimation Mizrach/Neely Tatonnement

  31. State Space Representation For the HUC model: 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. Mizrach/Neely Tatonnement

  32. Information Shares – Mapping From Structural Model Structural autocovariances: Reduced form: Moments matched: Solution: IS derived from these: Mizrach/Neely Tatonnement

  33. 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. Mizrach/Neely Tatonnement

  34. 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. Mizrach/Neely Tatonnement

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