IDEAS home Printed from https://ideas.repec.org/p/bca/bocawp/20-55.html
   My bibliography  Save this paper

Strategic Uncertainty in Financial Markets: Evidence from a Consensus Pricing Service

Author

Listed:
  • Lerby Ergun
  • Andreas Uthemann

Abstract

This paper measures valuation and strategic uncertainty in an over-the-counter market. The analysis uses a novel data set of price estimates that major financial institutions provide to a consensus pricing service. We model these institutions as Bayesian agents that learn from consensus prices about market conditions. Our uncertainty measures are derived from their beliefs through a structural estimation. The main contribution of the consensus pricing service is to reduce strategic uncertainty in the most opaque market segments. This stresses the importance of public data, such as financial benchmarks, for a shared understanding of market conditions in markets with limited price transparency.

Suggested Citation

  • Lerby Ergun & Andreas Uthemann, 2020. "Strategic Uncertainty in Financial Markets: Evidence from a Consensus Pricing Service," Staff Working Papers 20-55, Bank of Canada.
    • Handle: RePEc:bca:bocawp:20-55
    as

    Download full text from publisher

    File URL: https://www.bankofcanada.ca/wp-content/uploads/2020/12/swp2020-55.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Darrell Duffie & Piotr Dworczak & Haoxiang Zhu, 2017. "Benchmarks in Search Markets," Journal of Finance, American Finance Association, vol. 72(5), pages 1983-2044, October.
    2. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    3. George-Marios Angeletos & Alessandro Pavan, 2007. "Efficient Use of Information and Social Value of Information," Econometrica, Econometric Society, vol. 75(4), pages 1103-1142, July.
    4. Ali Hortaçsu & Jakub Kastl, 2012. "Valuing Dealers' Informational Advantage: A Study of Canadian Treasury Auctions," Econometrica, Econometric Society, vol. 80(6), pages 2511-2542, November.
    5. Sargent, Thomas J., 1991. "Equilibrium with signal extraction from endogenous variables," Journal of Economic Dynamics and Control, Elsevier, vol. 15(2), pages 245-273, April.
    6. Charles Cao & Eric Ghysels & Frank Hatheway, 2000. "Price Discovery without Trading: Evidence from the Nasdaq Preopening," Journal of Finance, American Finance Association, vol. 55(3), pages 1339-1365, June.
    7. Madhavan, Ananth & Panchapagesan, Venkatesh, 2000. "Price Discovery in Auction Markets: A Look Inside the Black Box," Review of Financial Studies, Society for Financial Studies, vol. 13(3), pages 627-658.
    8. Guido Lorenzoni, 2009. "A Theory of Demand Shocks," American Economic Review, American Economic Association, vol. 99(5), pages 2050-2084, December.
    9. Nina Boyarchenko & David O. Lucca & Laura Veldkamp, 2021. "Taking Orders and Taking Notes: Dealer Information Sharing in Treasury Auctions," Journal of Political Economy, University of Chicago Press, vol. 129(2), pages 607-645.
    10. Zhen Huo & Marcelo Pedroni, 2020. "A Single-Judge Solution to Beauty Contests," American Economic Review, American Economic Association, vol. 110(2), pages 526-568, February.
    11. Raith, Michael, 1996. "A General Model of Information Sharing in Oligopoly," Journal of Economic Theory, Elsevier, vol. 71(1), pages 260-288, October.
    12. Nimark, Kristoffer P., 2015. "A low dimensional Kalman filter for systems with lagged states in the measurement equation," Economics Letters, Elsevier, vol. 127(C), pages 10-13.
    13. Bruno Biais & Pierre Hillion & Chester Spatt, 1999. "Price Discovery and Learning during the Preopening Period in the Paris Bourse," Journal of Political Economy, University of Chicago Press, vol. 107(6), pages 1218-1248, December.
    Full references (including those not matched with items on IDEAS)

    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. Ergun, Lerby & Uthemann, Andreas, 2020. "Higher-order uncertainty in financial markets: evidence from a consensus pricing service," LSE Research Online Documents on Economics 118893, London School of Economics and Political Science, LSE Library.
    2. Benhima, Kenza, 2019. "Booms and busts with dispersed information," Journal of Monetary Economics, Elsevier, vol. 107(C), pages 32-47.
    3. Kristoffer Nimark, 2007. "Dynamic higher order expectations," Economics Working Papers 1118, Department of Economics and Business, Universitat Pompeu Fabra, revised Mar 2011.
    4. George-Marios Angeletos & Alessandro Pavan, 2009. "Policy with Dispersed Information," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 11-60, March.
    5. Jason Allen & Milena Wittwer, 2023. "Centralizing Over-the-Counter Markets?," Journal of Political Economy, University of Chicago Press, vol. 131(12), pages 3310-3351.
    6. Rondina, Giacomo & Walker, Todd B., 2021. "Confounding dynamics," Journal of Economic Theory, Elsevier, vol. 196(C).
    7. Chen, Tao & Cai, Jun & Ho, Richard Y.K., 2009. "Intraday information efficiency on the Chinese equity market," China Economic Review, Elsevier, vol. 20(3), pages 527-541, September.
    8. George‐Marios Angeletos & Fabrice Collard & Harris Dellas, 2018. "Quantifying Confidence," Econometrica, Econometric Society, vol. 86(5), pages 1689-1726, September.
    9. Ellul, Andrew & Shin, Hyun Song & Tonks, Ian, 2005. "Opening and Closing the Market: Evidence from the London Stock Exchange," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(4), pages 779-801, December.
    10. George-Marios Angeletos, 2018. "Frictional Coordination," Journal of the European Economic Association, European Economic Association, vol. 16(3), pages 563-603.
    11. Bellia, Mario & Pelizzon, Loriana & Subrahmanyam, Marti & Uno, Jun & Yuferova, Darya, 2017. "Coming early to the party," SAFE Working Paper Series 182, Leibniz Institute for Financial Research SAFE.
      • Mario Bellia & Loriana Pelizzon & Marti G. Subrahmanyam & Jun Uno & Darya Yuferova, 2020. "Coming early to the party," Working Papers 2020:11, Department of Economics, University of Venice "Ca' Foscari".
    12. Selma Boussetta, 2017. "The role of pre-opening mechanisms in fragmented markets," Post-Print hal-02156145, HAL.
    13. Anagnostidis, Panagiotis & Fontaine, Patrice & Varsakelis, Christos, 2020. "Are high–frequency traders informed?," Economic Modelling, Elsevier, vol. 93(C), pages 365-383.
    14. Roy, Nilanjan, 2017. "Action revision, information and collusion in an experimental duopoly market," MPRA Paper 77033, University Library of Munich, Germany.
    15. Cheremukhin, Anton & Tutino, Antonella, 2016. "Information rigidities and asymmetric business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 142-158.
    16. Ou, Shengliang & Zhang, Donghai & Zhang, Renbin, 2021. "Information frictions, monetary policy, and the paradox of price flexibility," Journal of Monetary Economics, Elsevier, vol. 120(C), pages 70-82.
    17. Angeletos, G.-M. & Lian, C., 2016. "Incomplete Information in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1065-1240, Elsevier.
    18. Robert Kelly, 2008. "Opening and Closing Asymmetry: Empirical Analysis from ISE Xetra," The Economic and Social Review, Economic and Social Studies, vol. 39(1), pages 55-78.
    19. Leonardo Melosi, 2017. "Signalling Effects of Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(2), pages 853-884.
    20. Spurlin, W. Paul & Van Ness, Bonnie F. & Van Ness, Robert A., 2008. "Open volume and time to open on option-expiration days," International Review of Economics & Finance, Elsevier, vol. 17(2), pages 245-257.

    More about this item

    Keywords

    Financial institutions; Financial markets; Market structure and pricing;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:bca:bocawp:20-55. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bocgvca.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.