[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v19y2021i3p395-430..html
   My bibliography  Save this article

Price Discovery in High Resolution

Author

Listed:
  • Joel Hasbrouck
Abstract
U.S. equity market data are currently timestamped to nanosecond precision. This permits models of price dynamics at resolutions sufficient to capture the reactions of the fastest agents. Direct estimation of multivariate time series models at sub-millisecond frequencies nevertheless poses substantial challenges. To facilitate such analyses, this paper applies long distributed lag models, computations that take advantage of the inherent sparsity of price transitions, and bridged modeling. At resolutions ranging from 1 s down to 10 μs, I estimate representative models for two stocks (IBM and NVDA) bearing on three topics of current interest. The first analysis examines the extent to which the conventional source of market data (the consolidated tape) accurately reflects the prices observed by agents who subscribe (at additional cost) to direct exchange feeds. At a 1-s resolution, the information share of the direct feeds is indistinguishable from that of the consolidated tape. At resolutions of 100 and 10 μs, however, the direct feeds are totally dominant, and the consolidated share approaches zero. The second analysis examines the quotes from the primary listing exchange vs. the non-listing exchanges. Here, too, information shares that are essentially indeterminate at 1-s resolution become much more distinct at higher resolutions. Although listing exchanges execute about one-fifth of the trading volume, their information shares are slightly above one-half. The third analysis examines quotes, lit trades, and dark trades. At a 1-s resolution, dark trades appear to have a small, but discernible, information contribution. This vanishes at higher resolutions. Quotes and lit trades essentially account for all price discovery, with information shares of roughly 65% and 35%, respectively.

Suggested Citation

  • Joel Hasbrouck, 2021. "Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 395-430.
    • Handle: RePEc:oup:jfinec:v:19:y:2021:i:3:p:395-430.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbz027
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stock, James H & Watson, Mark W, 1988. "Variable Trends in Economic Time Series," Journal of Economic Perspectives, American Economic Association, vol. 2(3), pages 147-174, Summer.
    2. Kwan, Amy & Masulis, Ronald & McInish, Thomas H., 2015. "Trading rules, competition for order flow and market fragmentation," Journal of Financial Economics, Elsevier, vol. 115(2), pages 330-348.
    3. Dufour, Alfonso & Engle, Robert F, 1999. "Time and the Price Impact of a Trade," University of California at San Diego, Economics Working Paper Series qt62c0h04j, Department of Economics, UC San Diego.
    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    5. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    6. Joel Hasbrouck, 1999. "Trading Fast and Slow: Security Market Events in Real Time," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-012, New York University, Leonard N. Stern School of Business-.
    7. Frank De Jong & Peter C. Schotman, 2010. "Price Discovery in Fragmented Markets," Journal of Financial Econometrics, Oxford University Press, vol. 8(1), pages 1-28, Winter.
    8. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    9. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2019. "Price Discovery without Trading: Evidence from Limit Orders," Journal of Finance, American Finance Association, vol. 74(4), pages 1621-1658, August.
    10. Bloomfield, Robert & O'Hara, Maureen & Saar, Gideon, 2005. "The "make or take" decision in an electronic market: Evidence on the evolution of liquidity," Journal of Financial Economics, Elsevier, vol. 75(1), pages 165-199, January.
    11. de Jong, Frank, 2002. "Measures of contributions to price discovery: a comparison," Journal of Financial Markets, Elsevier, vol. 5(3), pages 323-327, July.
    12. Clark-Joseph, Adam D. & Ye, Mao & Zi, Chao, 2017. "Designated market makers still matter: Evidence from two natural experiments," Journal of Financial Economics, Elsevier, vol. 126(3), pages 652-667.
    13. deB. Harris, Frederick H. & McInish, Thomas H. & Shoesmith, Gary L. & Wood, Robert A., 1995. "Cointegration, Error Correction, and Price Discovery on Informationally Linked Security Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(4), pages 563-579, December.
    14. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    15. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    16. Michael Goldstein & Shengwei Ding & John Hanna & Terrence Hendershott, 2014. "How Slow Is the NBBO? A Comparison with Direct Exchange Feeds," The Financial Review, Eastern Finance Association, vol. 49(2), pages 313-332, May.
    17. Alfonso Dufour & Robert F. Engle, 2000. "Time and the Price Impact of a Trade," Journal of Finance, American Finance Association, vol. 55(6), pages 2467-2498, December.
    18. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    19. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 31-67.
    20. Hatheway, Frank & Kwan, Amy & Zheng, Hui, 2017. "An Empirical Analysis of Market Segmentation on U.S. Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2399-2427, December.
    21. O'Hara, Maureen & Ye, Mao, 2011. "Is market fragmentation harming market quality?," Journal of Financial Economics, Elsevier, vol. 100(3), pages 459-474, June.
    22. deB. Harris, Frederick H. & McInish, Thomas H. & Wood, Robert A., 2002. "Security price adjustment across exchanges: an investigation of common factor components for Dow stocks," Journal of Financial Markets, Elsevier, vol. 5(3), pages 277-308, July.
    23. Ozturk, Sait R. & van der Wel, Michel & van Dijk, Dick, 2017. "Intraday price discovery in fragmented markets," Journal of Financial Markets, Elsevier, vol. 32(C), pages 28-48.
    24. Kumar, Praveen & Seppi, Duane J, 1994. "Information and Index Arbitrage," The Journal of Business, University of Chicago Press, vol. 67(4), pages 481-509, October.
    25. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    26. Joel Hasbrouck, 2003. "Intraday Price Formation in U.S. Equity Index Markets," Journal of Finance, American Finance Association, vol. 58(6), pages 2375-2400, December.
    27. Putniņš, Tālis J., 2013. "What do price discovery metrics really measure?," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 68-83.
    28. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    29. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    30. Grammig, Joachim & Peter, Franziska J., 2013. "Telltale Tails: A New Approach to Estimating Unique Market Information Shares," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(2), pages 459-488, April.
    31. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    32. Hasbrouck, Joel, 1995. "One Security, Many Markets: Determining the Contributions to Price Discovery," Journal of Finance, American Finance Association, vol. 50(4), pages 1175-1199, September.
    33. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
    34. Lehmann, Bruce N., 2002. "Some desiderata for the measurement of price discovery across markets," Journal of Financial Markets, Elsevier, vol. 5(3), pages 259-276, July.
    35. Hasbrouck, Joel, 2018. "High-Frequency Quoting: Short-Term Volatility in Bids and Offers," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(2), pages 613-641, April.
    36. de Jong, F.C.J.M. & Schotman, P.C., 2010. "Price discovery in fragmented markets," Other publications TiSEM 4650a9e7-c4cf-41cf-a771-e, Tilburg University, School of Economics and Management.
    37. Yanping Chong & Òscar Jordà & Alan M. Taylor, 2012. "The Harrod–Balassa–Samuelson Hypothesis: Real Exchange Rates And Their Long‐Run Equilibrium," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(2), pages 609-634, May.
    38. Terrence Hendershott & Charles M. Jones, 2005. "Island Goes Dark: Transparency, Fragmentation, and Regulation," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 743-793.
    39. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    40. Yan, Bingcheng & Zivot, Eric, 2010. "A structural analysis of price discovery measures," Journal of Financial Markets, Elsevier, vol. 13(1), pages 1-19, February.
    41. Bartlett, Robert P. & McCrary, Justin, 2019. "How rigged are stock markets? Evidence from microsecond timestamps," Journal of Financial Markets, Elsevier, vol. 45(C), pages 37-60.
    42. Hasbrouck, Joel, 2002. "Stalking the "efficient price" in market microstructure specifications: an overview," Journal of Financial Markets, Elsevier, vol. 5(3), pages 329-339, July.
    43. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2018. "Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions," Journal of Econometrics, Elsevier, vol. 207(1), pages 71-91.
    44. Baillie, Richard T. & Geoffrey Booth, G. & Tse, Yiuman & Zabotina, Tatyana, 2002. "Price discovery and common factor models," Journal of Financial Markets, Elsevier, vol. 5(3), pages 309-321, July.
    45. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    46. Pascual, Roberto & Pascual-Fuster, Bartolomé, 2014. "The relative contribution of ask and bid quotes to price discovery," Journal of Financial Markets, Elsevier, vol. 20(C), pages 129-150.
    47. Easley, David & O'Hara, Maureen, 1992. "Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
    48. Shephard, Neil (ed.), 2005. "Stochastic Volatility: Selected Readings," OUP Catalogue, Oxford University Press, number 9780199257201.
    49. Björn Hagströmer & Albert J. Menkveld, 2019. "Information Revelation in Decentralized Markets," Journal of Finance, American Finance Association, vol. 74(6), pages 2751-2787, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sebastiano Michele Zema & Francesco Cordoni, 2023. "A non-Normal framework for price discovery: The independent component based information shares measure," LEM Papers Series 2023/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Poutré, Cédric & Dionne, Georges & Yergeau, Gabriel, 2024. "The profitability of lead–lag arbitrage at high frequency," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1002-1021.
    3. Kuck, Konstantin & Schweikert, Karsten, 2023. "Price discovery in equity markets: A state-dependent analysis of spot and futures markets," Journal of Banking & Finance, Elsevier, vol. 149(C).
    4. Yan, Tingjin & Chiu, Mei Choi & Wong, Hoi Ying, 2023. "Portfolio liquidation with delayed information," Economic Modelling, Elsevier, vol. 126(C).
    5. Liwei Jin & Xianghui Yuan & Shihao Wang & Peiran Li & Feng Lian, 2022. "Trades or quotes: Which drives price discovery? Evidence from Chinese index futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2235-2247, December.
    6. Dimpfl, Thomas & Schweikert, Karsten, 2023. "Information shares for markets with partially overlapping trading hours," Journal of Banking & Finance, Elsevier, vol. 154(C).
    7. Sagade, Satchit & Scharnowski, Stefan & Theissen, Erik & Westheide, Christian, 2024. "A tale of two cities: Inter-market latency and fast-trader competition," SAFE Working Paper Series 430, Leibniz Institute for Financial Research SAFE.
    8. Zema, Sebastiano Michele, 2022. "Directed acyclic graph based information shares for price discovery," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    9. Peter B. Lerner, 2023. "A New Entropic Measure for the Causality of the Financial Time Series," JRFM, MDPI, vol. 16(7), pages 1-17, July.
    10. F. Campigli & G. Bormetti & F. Lillo, 2022. "Measuring price impact and information content of trades in a time-varying setting," Papers 2212.12687, arXiv.org, revised Dec 2023.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zema, Sebastiano Michele, 2022. "Directed acyclic graph based information shares for price discovery," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    2. Sebastiano Michele Zema, 2020. "Directed Acyclic Graph based Information Shares for Price Discovery," LEM Papers Series 2020/28, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Hou, Yang & Li, Steven, 2017. "Time-Varying Price Discovery and Autoregressive Loading Factors: Evidence from S&P 500 Cash and E-Mini Futures Markets," MPRA Paper 81999, University Library of Munich, Germany.
    4. Donald Lien & Zijun Wang, 2016. "Estimation of Market Information Shares: A Comparison," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(11), pages 1108-1124, November.
    5. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    6. Vinay Patel, 2015. "Price Discovery in US and Australian Stock and Options Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 27, July-Dece.
    7. Vinay Patel, 2015. "Price Discovery in US and Australian Stock and Options Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 6-2015, January-A.
    8. Liwei Jin & Xianghui Yuan & Shihao Wang & Peiran Li & Feng Lian, 2022. "Trades or quotes: Which drives price discovery? Evidence from Chinese index futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2235-2247, December.
    9. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.
    10. Zebedee, Allan A. & Kasch-Haroutounian, Maria, 2009. "A closer look at co-movements among stock returns," Journal of Economics and Business, Elsevier, vol. 61(4), pages 279-294, July.
    11. Mehdi Arzandeh & Julieta Frank, 2019. "Price Discovery in Agricultural Futures Markets: Should We Look beyond the Best Bid-Ask Spread?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(5), pages 1482-1498.
    12. Hatheway, Frank & Kwan, Amy & Zheng, Hui, 2017. "An Empirical Analysis of Market Segmentation on U.S. Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2399-2427, December.
    13. Sebastiano Michele Zema & Francesco Cordoni, 2023. "A non-Normal framework for price discovery: The independent component based information shares measure," LEM Papers Series 2023/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    14. Qin Wang & Hsiao-Fen Yang, 2015. "Earnings announcements, trading volume, and price discovery: evidence from dual class firms," Review of Quantitative Finance and Accounting, Springer, vol. 44(4), pages 669-700, May.
    15. Christoph Schmidhammer & Sebastian Lobe & Klaus Röder, 2016. "The day the index rose 11 %: a clinical study on price discovery reversal," Review of Quantitative Finance and Accounting, Springer, vol. 46(1), pages 79-106, January.
    16. Chen, Wei-Peng & Chung, Huimin & Lien, Donald, 2016. "Price discovery in the S&P 500 index derivatives markets," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 438-452.
    17. Damien Wallace & Petko S. Kalev & Guanhua Lian, 2019. "The evolution of price discovery in us equity and derivatives markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1122-1136, September.
    18. Santos, Francisco Luna & Garcia, Márcio Gomes Pinto & Medeiros, Marcelo Cunha, 2015. "Price Discovery in Brazilian FX Markets," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.
    19. Yang Hou & Steven Li & Fenghua Wen, 2021. "Time-varying information share and autoregressive loading factors: evidence from S&P 500 cash and E-mini futures markets," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 91-110, July.
    20. Bingcheng Yan & Eric Zivot, 2007. "A Structural Analysis of Price Discovery Measures," Working Papers UWEC-2006-08-FC, University of Washington, Department of Economics, revised Apr 2007.

    More about this item

    Keywords

    high frequency trading; high resolution; polynomial distributed lags; sparsity; vector autoregression (VAR); vector error correction models (VECMs);
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:jfinec:v:19:y:2021:i:3:p:395-430.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.