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An Ordered Probit Analysis of Transaction Stock Prices (Reprint 029)

Author

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  • Jerry A. Hausman
  • Andrew W. Lo
  • Craig A. MacKinlay

Abstract

We estimate the conditional distribution of trade-to-trade price changes using ordered probit, a statistical model for discrete random variables. Such an approach takes into account the fact that transaction price changes occur in discrete increments, typically eighths of a dollar, and occur at irregularly spaced time intervals. Unlike existing continuous-time/discrete-state models of discrete transaction prices, ordered probit can capture the effects of other economic variables on price exchanges, such as volume, past price changes, and the time between trades. Using 1988 transactions data for over 100 randomly chosen U.S. stocks, we estimate the ordered probit model via maximum likelihood and use the parameter estimates to measure several transaction-related quantities, such as the price impact of trades of a given size, the tendency towards price reversals from one transaction to the next, and the empirical significance of price discreteness.

Suggested Citation

  • Jerry A. Hausman & Andrew W. Lo & Craig A. MacKinlay, "undated". "An Ordered Probit Analysis of Transaction Stock Prices (Reprint 029)," Rodney L. White Center for Financial Research Working Papers 26-91, Wharton School Rodney L. White Center for Financial Research.
    • Handle: RePEc:fth:pennfi:26-91
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    Citations

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    Cited by:

    1. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    2. Debdulal Thakur & Shrabani Mukherjee, 2016. "Parents’ Choice Function for Wards’ School Continuation in Rural India: A Case Study in West Bengal," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 10(1), pages 119-142, February.
    3. Mi Yung Yoon, 1997. "Explaining U.S. Intervention in Third World Internal Wars, 1945-1989," Journal of Conflict Resolution, Peace Science Society (International), vol. 41(4), pages 580-602, August.
    4. Ghysels, E. & Jasiak, J., 1994. "Stochastic Volatility and time Deformation: an Application of trading Volume and Leverage Effects," Cahiers de recherche 9403, Universite de Montreal, Departement de sciences economiques.
    5. Shrabani Mukherjee, 2015. "Entrepreneurial Choice of Investment Capital for House-based Industries," Review of Market Integration, India Development Foundation, vol. 7(3), pages 214-241, December.
    6. Koopman, S.J.M. & Lai, H.N., 1998. "Modelling bid-ask spreads in competitive dealership markets," Other publications TiSEM 7a193911-dbf2-4831-ac8d-9, Tilburg University, School of Economics and Management.
    7. Stephan Grimm & Thomas Guhr, 2018. "How spread changes affect the order book: Comparing the price responses of order deletions and placements to trades," Papers 1812.09067, arXiv.org.
    8. Stephan Grimm & Thomas Guhr, 2019. "How spread changes affect the order book: comparing the price responses of order deletions and placements to trades," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(6), pages 1-11, June.
    9. González, M. & Minguez, R., 2005. "The Method Of Simulated Maximum Likelihood For The Estimaton Of Dynamic Ordered Probit: An Application To Country-Risk For Non-Developed Countries," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(3), pages 99-133.
    10. Shrabani Mukherjee & Keshav Sood, 2020. "Triggers and Barriers of Financial Inclusion: A Country-Wise Analysis," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(9), pages 970-988, September.

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