IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/118893.html
   My bibliography  Save this paper

Higher-order uncertainty in financial markets: evidence from a consensus pricing service

Author

Listed:
  • Ergun, Lerby
  • Uthemann, Andreas

Abstract

We assess the ability of an information aggregation mechanism that operates in the over-the-counter market for financial derivatives to reduce valuation uncertainty among market participants. The analysis is based on a unique dataset of price estimates for S&P 500 index options that major financial institutions provide to a consensus pricing service. We consider two dimensions of uncertainty: uncertainty about fundamental asset values and strategic uncertainty about competitors' valuations. Through structural estimation, we obtain empirical measures of fundamental and strategic uncertainty that are based on market participants' posterior beliefs. We show that the main contribution of the consensus pricing service is to reduce its subscribers' uncertainty about competitors' valuations.

Suggested Citation

  • 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.
    • Handle: RePEc:ehl:lserod:118893
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/118893/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    2. Darrell Duffie & Piotr Dworczak & Haoxiang Zhu, 2017. "Benchmarks in Search Markets," Journal of Finance, American Finance Association, vol. 72(5), pages 1983-2044, October.
    3. Ali Hortaçsu & Jakub Kastl & Allen Zhang, 2018. "Bid Shading and Bidder Surplus in the US Treasury Auction System," American Economic Review, American Economic Association, vol. 108(1), pages 147-169, January.
    4. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    5. Nicolae Garleanu & Lasse Heje Pedersen & Allen M. Poteshman, 2009. "Demand-Based Option Pricing," The Review of Financial Studies, Society for Financial Studies, vol. 22(10), pages 4259-4299, October.
    6. Hellwig, Martin F., 1980. "On the aggregation of information in competitive markets," Journal of Economic Theory, Elsevier, vol. 22(3), pages 477-498, June.
    7. Marzena Rostek & Marek Weretka, 2012. "Price Inference in Small Markets," Econometrica, Econometric Society, vol. 80(2), pages 687-711, March.
    8. 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.
    9. Kenneth Kasa, 2000. "Forecasting the Forecasts of Others in the Frequency Domain," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 3(4), pages 726-756, October.
    10. Vives, Xavier, 1997. "Learning from Others: A Welfare Analysis," Games and Economic Behavior, Elsevier, vol. 20(2), pages 177-200, August.
    11. Ilan Kremer, 2002. "Information Aggregation in Common Value Auctions," Econometrica, Econometric Society, vol. 70(4), pages 1675-1682, July.
    12. 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.
    13. Gale, Douglas M, 1986. "Bargaining and Competition Part I: Characterization," Econometrica, Econometric Society, vol. 54(4), pages 785-806, July.
    14. 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.
    15. Raith, Michael, 1996. "A General Model of Information Sharing in Oligopoly," Journal of Economic Theory, Elsevier, vol. 71(1), pages 260-288, October.
    16. Rajiv Sethi & Muhamet Yildiz, 2016. "Communication With Unknown Perspectives," Econometrica, Econometric Society, vol. 84, pages 2029-2069, November.
    17. Amador, Manuel & Weill, Pierre-Olivier, 2012. "Learning from private and public observations of othersʼ actions," Journal of Economic Theory, Elsevier, vol. 147(3), pages 910-940.
    18. 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.
    19. Easley, David, et al, 1996. "Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    20. Stephen Morris & Hyun Song Shin, 2002. "Social Value of Public Information," American Economic Review, American Economic Association, vol. 92(5), pages 1521-1534, December.
    21. 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.
    22. Townsend, Robert M, 1983. "Forecasting the Forecasts of Others," Journal of Political Economy, University of Chicago Press, vol. 91(4), pages 546-588, August.
    23. Diamond, Douglas W. & Verrecchia, Robert E., 1981. "Information aggregation in a noisy rational expectations economy," Journal of Financial Economics, Elsevier, vol. 9(3), pages 221-235, September.
    24. Madhavan, Ananth & Panchapagesan, Venkatesh, 2000. "Price Discovery in Auction Markets: A Look Inside the Black Box," The Review of Financial Studies, Society for Financial Studies, vol. 13(3), pages 627-658.
    25. 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. Lerby Ergun & Andreas Uthemann, 2020. "Strategic Uncertainty in Financial Markets: Evidence from a Consensus Pricing Service," Staff Working Papers 20-55, Bank of Canada.
    2. Mäkinen, Taneli & Ohl, Björn, 2015. "Information acquisition and learning from prices over the business cycle," Journal of Economic Theory, Elsevier, vol. 158(PB), pages 585-633.
    3. 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.
    4. Pavan, Alessandro & Vives, Xavier, 2015. "Information, Coordination, and Market Frictions: An Introduction," Journal of Economic Theory, Elsevier, vol. 158(PB), pages 407-426.
    5. Philippe Bacchetta & Eric Van Wincoop, 2006. "Can Information Heterogeneity Explain the Exchange Rate Determination Puzzle?," American Economic Review, American Economic Association, vol. 96(3), pages 552-576, June.
    6. Michele Berardi, 2021. "Learning from prices: information aggregation and accumulation in an asset market," Annals of Finance, Springer, vol. 17(1), pages 45-77, March.
    7. Anagnostidis, Panagiotis & Fontaine, Patrice & Varsakelis, Christos, 2020. "Are high–frequency traders informed?," Economic Modelling, Elsevier, vol. 93(C), pages 365-383.
    8. Panagiotis Anagnostidis & Patrice Fontaine & Christos Varsakelis, 2020. "Are high–frequency traders informed?," Post-Print hal-03062831, HAL.
    9. Huo, Zhen & Pedroni, Marcelo, 2023. "Dynamic information aggregation: Learning from the past," Journal of Monetary Economics, Elsevier, vol. 136(C), pages 107-124.
    10. 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.
    11. Makarov, Igor & Rytchkov, Oleg, 2012. "Forecasting the forecasts of others: Implications for asset pricing," Journal of Economic Theory, Elsevier, vol. 147(3), pages 941-966.
    12. Asriyan, Vladimir & Fuchs, William & Green, Brett, 2021. "Aggregation and design of information in asset markets with adverse selection," Journal of Economic Theory, Elsevier, vol. 191(C).
    13. Spyros Pagratis, 2005. "Asset pricing, asymmetric information and rating announcements: does benchmarking on ratings matter?," Bank of England working papers 265, Bank of England.
    14. Xavier Vives, 2017. "Endogenous Public Information and Welfare in Market Games," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(2), pages 935-963.
    15. Anna Bayona, 2018. "The social value of information with an endogenous public signal," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 66(4), pages 1059-1087, December.
    16. Kristoffer Nimark, 2009. "Speculative dynamics in the term structure of interest rates," Economics Working Papers 1194, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2012.
    17. Tarek A. Hassan & Thomas M. Mertens, 2017. "The Social Cost of Near-Rational Investment," American Economic Review, American Economic Association, vol. 107(4), pages 1059-1103, April.
    18. Lou, Youcheng & Parsa, Sahar & Ray, Debraj & Li, Duan & Wang, Shouyang, 2019. "Information aggregation in a financial market with general signal structure," Journal of Economic Theory, Elsevier, vol. 183(C), pages 594-624.
    19. George-Marios Angeletos & Chen Lian, 2016. "Incomplete Information in Macroeconomics: Accommodating Frictions in Coordination," NBER Working Papers 22297, National Bureau of Economic Research, Inc.
    20. Michele Berardi, 2020. "Learning from Prices: Information Aggregation and Accumulation in an Asset Price Model," Economics Discussion Paper Series 2009, Economics, The University of Manchester.

    More about this item

    Keywords

    OTC markets; information aggregation; social learning; strategic uncertainty; consensus pricing; benchmarks;
    All these keywords.

    JEL classification:

    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
    • 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:ehl:lserod:118893. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.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.