IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/79459.html
   My bibliography  Save this paper

Corwin-Schultz bid-ask spread estimator in the Brazilian stock market

Author

Listed:
  • Ripamonti, Alexandre

Abstract

This paper tests the validity of the Corwin-Schultz bid-ask spread estimator in the Brazilian stock market. The Corwin-Schultz estimator arises as an easy way to compute asymmetric information throughout daily high and low stock prices for estimating overnight and non-negative adjusted spreads. The sample consisted of Ibovespa firms from 1986 to 2014 and was analysed with time series econometrics. The findings show that the measures of spread have stationarity properties, allowing for forecasting in a period of lagged variables, besides having the property of time-varying cointegration with market-to-book ratio, debt on equity, size and return and also presenting sensibility to different periods, industries and listing segments. Thus, the Corwin-Schultz bid-ask spread estimator seems to be a valid and reliable measure for forecasting aggregate-data variables through the weighted average of firm-level variables.

Suggested Citation

  • Ripamonti, Alexandre, 2016. "Corwin-Schultz bid-ask spread estimator in the Brazilian stock market," MPRA Paper 79459, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:79459
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/79459/8/MPRA_paper_79459.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. David Easley & Soeren Hvidkjaer & Maureen O'Hara, 2002. "Is Information Risk a Determinant of Asset Returns?," Journal of Finance, American Finance Association, vol. 57(5), pages 2185-2221, October.
    3. Karstanje, Dennis & Sojli, Elvira & Tham, Wing Wah & van der Wel, Michel, 2013. "Economic valuation of liquidity timing," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5073-5087.
    4. 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.
    5. 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.
    6. J. Barkley Rosser, 2003. "A Nobel Prize for Asymmetric Information: The economic contributions of George Akerlof, Michael Spence and Joseph Stiglitz," Review of Political Economy, Taylor & Francis Journals, vol. 15(1), pages 3-21.
    7. Hasbrouck, Joel, 2007. "Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading," OUP Catalogue, Oxford University Press, number 9780195301649.
    8. 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.
    9. Xindong Zhang & Junxian Yang & Huimin Su & Shun Zhang, 2014. "Liquidity premium and the Corwin-Schultz bid-ask spread estimate," China Finance Review International, Emerald Group Publishing Limited, vol. 4(2), pages 168-186, May.
    10. Huang, Roger D & Stoll, Hans R, 1997. "The Components of the Bid-Ask Spread: A General Approach," The Review of Financial Studies, Society for Financial Studies, vol. 10(4), pages 995-1034.
    11. Easley, David & Kiefer, Nicholas M & O'Hara, Maureen, 1997. "One Day in the Life of a Very Common Stock," The Review of Financial Studies, Society for Financial Studies, vol. 10(3), pages 805-835.
    12. Roll, Richard & Schwartz, Eduardo & Subrahmanyam, Avanidhar, 2014. "Trading activity in the equity market and its contingent claims: An empirical investigation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 13-35.
    13. Bierens, Herman J. & Martins, Luis F., 2010. "Time-Varying Cointegration," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1453-1490, October.
    14. 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.
    15. Demirguc-Kunt, Ash & Maksimovic, Vojislav, 1996. "Stock Market Development and Financing Choices of Firms," The World Bank Economic Review, World Bank, vol. 10(2), pages 341-369, May.
    16. Hasbrouck, Joel & Saar, Gideon, 2009. "Technology and liquidity provision: The blurring of traditional definitions," Journal of Financial Markets, Elsevier, vol. 12(2), pages 143-172, May.
    17. Madhavan, Ananth, 2000. "Market microstructure: A survey," Journal of Financial Markets, Elsevier, vol. 3(3), pages 205-258, August.
    18. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
    19. Krinsky, Itzhak & Lee, Jason, 1996. "Earnings Announcements and the Components of the Bid-Ask Spread," Journal of Finance, American Finance Association, vol. 51(4), pages 1523-1535, September.
    20. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    21. Bailey,Roy E., 2005. "The Economics of Financial Markets," Cambridge Books, Cambridge University Press, number 9780521612807.
    22. Hasbrouck, Joel, 1999. "Security bid/ask dynamics with discreteness and clustering: Simple strategies for modeling and estimation1," Journal of Financial Markets, Elsevier, vol. 2(1), pages 1-28, February.
    23. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    24. David Easley & Maureen O'hara, 2004. "Information and the Cost of Capital," Journal of Finance, American Finance Association, vol. 59(4), pages 1553-1583, August.
    25. George A. Akerlof, 1970. "The Market for "Lemons": Quality Uncertainty and the Market Mechanism," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 84(3), pages 488-500.
    26. 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.
    27. Hasbrouck, Joel & Seppi, Duane J., 2001. "Common factors in prices, order flows, and liquidity," Journal of Financial Economics, Elsevier, vol. 59(3), pages 383-411, March.
    28. Kalok Chan & Albert J. Menkveld & Zhishu Yang, 2008. "Information Asymmetry and Asset Prices: Evidence from the China Foreign Share Discount," Journal of Finance, American Finance Association, vol. 63(1), pages 159-196, February.
    29. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    30. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    31. Timmermann, Allan, 1995. "Cointegration Tests of Present Value Models with a Time-Varying Discount Factor," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(1), pages 17-31, Jan.-Marc.
    32. Maskara, Pankaj K. & Mullineaux, Donald J., 2011. "Information asymmetry and self-selection bias in bank loan announcement studies," Journal of Financial Economics, Elsevier, vol. 101(3), pages 684-694, September.
    33. Granger, Clive W.J., 2010. "Some thoughts on the development of cointegration," Journal of Econometrics, Elsevier, vol. 158(1), pages 3-6, September.
    34. Titman, Sheridan & Wessels, Roberto, 1988. " The Determinants of Capital Structure Choice," Journal of Finance, American Finance Association, vol. 43(1), pages 1-19, March.
    35. Hasbrouck, Joel, 1988. "Trades, quotes, inventories, and information," Journal of Financial Economics, Elsevier, vol. 22(2), pages 229-252, December.
    36. Michael Rothschild & Joseph Stiglitz, 1976. "Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 90(4), pages 629-649.
    37. Shane A. Corwin & Paul Schultz, 2012. "A Simple Way to Estimate Bid‐Ask Spreads from Daily High and Low Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 719-760, April.
    38. Roll, Richard & Subrahmanyam, Avanidhar, 2010. "Liquidity skewness," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2562-2571, October.
    39. Easley, David & O'Hara, Maureen, 1991. "Order Form and Information in Securities Markets," Journal of Finance, American Finance Association, vol. 46(3), pages 905-927, July.
    40. Roll, Richard, 1984. "A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market," Journal of Finance, American Finance Association, vol. 39(4), pages 1127-1139, September.
    41. Easley, David, et al, 1996. "Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    42. Orleans Silva Martins & Edilson Paulo, 2013. "The probability of informed trading in the Brazilian stock market," Brazilian Review of Finance, Brazilian Society of Finance, vol. 11(2), pages 249-280.
    43. David Easley & Maureen O'Hara & P.S. Srinivas, 1998. "Option Volume and Stock Prices: Evidence on Where Informed Traders Trade," Journal of Finance, American Finance Association, vol. 53(2), pages 431-465, April.
    44. Harris, Lawrence, 1990. "Statistical Properties of the Roll Serial Covariance Bid/Ask Spread Estimator," Journal of Finance, American Finance Association, vol. 45(2), pages 579-590, June.
    45. I. Krinsky & J. Lee, 1996. "Earning Announcements and the Components of the Bid-Ask Aspread," Quantitative Studies in Economics and Population Research Reports 313, McMaster University.
    46. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(3), pages 355-374.
    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. Ripamonti, Alexandre, 2020. "Financial institutions, asymmetric information and capital structure adjustments," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 75-83.
    2. Ripamonti, Alexandre & Silva, Diego & Moreira Neto, Eurico, 2018. "Asset Pricing and Asymmetric Information," MPRA Paper 87403, University Library of Munich, Germany.
    3. Ripamonti, Alexandre, 2019. "Capital Structure Adjustments and Asymmetric Information," MPRA Paper 96936, University Library of Munich, Germany.
    4. Aritra Pan & Arun Kumar Misra & David McMillan, 2021. "A comprehensive study on bid-ask spread and its determinants in India," Cogent Economics & Finance, Taylor & Francis Journals, vol. 9(1), pages 1898735-189, January.
    5. Alexandre Ripamonti & Raphael Videira & Denis Ichimura, 2020. "Asymmetric information and daily stock prices in Brazil," Estudios Gerenciales, Universidad Icesi, vol. 36(157), pages 465-472, December.

    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. Ripamonti, Alexandre, 2019. "Capital Structure Adjustments and Asymmetric Information," MPRA Paper 96936, University Library of Munich, Germany.
    2. Ripamonti, Alexandre & Silva, Diego & Moreira Neto, Eurico, 2018. "Asset Pricing and Asymmetric Information," MPRA Paper 87403, University Library of Munich, Germany.
    3. Bardong, Florian & Bartram, Söhnke M. & Yadav, Pradeep K., 2005. "Informed Trading, Information Asymmetry and Pricing of Information Risk: Empirical Evidence from the NYSE," MPRA Paper 13586, University Library of Munich, Germany, revised 10 Oct 2008.
    4. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    5. Alexandre Ripamonti, 2019. "Capital Structure Adjustments and Asymmetric Information," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(12), pages 1-1, December.
    6. Vayanos, Dimitri & Wang, Jiang, 2013. "Market Liquidity—Theory and Empirical Evidence ," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1289-1361, Elsevier.
    7. Pascual, Roberto & Escribano, Álvaro & Tapia, Mikel, 2000. "Adverse selection costs, trading activity and liquidity in the NYSE: an empirical analysis in a dynamic context," UC3M Working papers. Economics 7276, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Biais, Bruno & Glosten, Larry & Spatt, Chester, 2005. "Market microstructure: A survey of microfoundations, empirical results, and policy implications," Journal of Financial Markets, Elsevier, vol. 8(2), pages 217-264, May.
    9. Ripamonti, Alexandre, 2020. "Financial institutions, asymmetric information and capital structure adjustments," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 75-83.
    10. Jagjeev Dosanjh, 2017. "Exchange Initiatives and Market Efficiency: Evidence from the Australian Securities Exchange," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2017.
    11. repec:uts:finphd:34 is not listed on IDEAS
    12. Thanh Huong Nguyen, 2019. "Information and Noise in Stock Markets: Evidence on the Determinants and Effects Using New Empirical Measures," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 7-2019.
    13. Sankaraguruswamy, Srinivasan & Shen, Jianfeng & Yamada, Takeshi, 2013. "The relationship between the frequency of news release and the information asymmetry: The role of uninformed trading," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4134-4143.
    14. Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
    15. Bryan Kelly & Alexander Ljungqvist, 2012. "Testing Asymmetric-Information Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1366-1413.
    16. Thomas Pöppe & Michael Aitken & Dirk Schiereck & Ingo Wiegand, 2016. "A PIN per day shows what news convey: the intraday probability of informed trading," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1187-1220, November.
    17. Lamoureux, Christopher G. & Wang, Qin, 2015. "Measuring private information in a specialist market," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 92-119.
    18. Berkman, Henk & Koch, Paul D., 2008. "Noise trading and the price formation process," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 232-250, March.
    19. Arango, Ignacio & Agudelo, Diego A., 2019. "How does information disclosure affect liquidity? Evidence from an emerging market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    20. Acheson, Graeme G. & Coyle, Christopher & Turner, John D., 2018. "Prices and informed trading: Evidence from an early stock market," QUCEH Working Paper Series 2018-05, Queen's University Belfast, Queen's University Centre for Economic History.
    21. Craig W. Holden & Stacey Jacobsen & Avanidhar Subrahmanyam, 2014. "The Empirical Analysis of Liquidity," Foundations and Trends(R) in Finance, now publishers, vol. 8(4), pages 263-365, December.

    More about this item

    Keywords

    Corwin-Schultz bid-ask spread estimator; asymmetric information; market microstructure; time varying cointegration;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G39 - Financial Economics - - Corporate Finance and Governance - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:79459. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.