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Estimating liquidity using information on the multivariate trading process


  • Katarzyna Bien

    (Warsaw School of Economics)

  • Ingmar Nolte

    (University of Konstanz, CoFE)

  • Winfried Pohlmeier

    (University of Konstanz, CoFE, ZEW)


In this paper we model the dynamic multivariate density of discrete bid and ask quote changes and their associated depths. We account for the contempo- raneous relationship between these trading marks by exploiting the concept of copula functions. Thereby we show how to model truncations of the mul- tivariate density in an easy way. A Metropolized-Independence Sampler is applied to draw from the dynamic multivariate density. The samples drawn serve to construct the dynamic density function of the quote slope liquidity measure, which enables us to quantify time varying liquidity risk. We analyze the influence of the decimalization at the NYSE on liquidity.

Suggested Citation

  • Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2006. "Estimating liquidity using information on the multivariate trading process," Working Papers 10, Department of Applied Econometrics, Warsaw School of Economics.
  • Handle: RePEc:wse:wpaper:10

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    References listed on IDEAS

    1. Michaely, Roni & Vila, Jean-Luc & Wang, Jiang, 1996. "A Model of Trading Volume with Tax-Induced Heterogeneous Valuation and Transaction Costs," Journal of Financial Intermediation, Elsevier, vol. 5(4), pages 340-371, October.
    2. Sugato Chakravarty & Robert A. Wood & Robert A. Van Ness, 2004. "Decimals And Liquidity: A Study Of The Nyse," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 27(1), pages 75-94.
    3. Tina Hviid Rydberg & Neil Shephard, 2003. "Dynamics of Trade-by-Trade Price Movements: Decomposition and Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 1(1), pages 2-25.
    4. Engle, Robert F. & Lange, Joe, 2001. "Predicting VNET: A model of the dynamics of market depth," Journal of Financial Markets, Elsevier, vol. 4(2), pages 113-142, April.
    5. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    6. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    7. A. Colin Cameron & Tong Li & Pravin K. Trivedi & David M. Zimmer, 2004. "Modelling the differences in counted outcomes using bivariate copula models with application to mismeasured counts," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 566-584, December.
    8. Hausman, Jerry A. & Lo, Andrew W. & MacKinlay, A. Craig, 1992. "An ordered probit analysis of transaction stock prices," Journal of Financial Economics, Elsevier, vol. 31(3), pages 319-379, June.
    9. Fernando, Chitru S., 2003. "Commonality in liquidity: transmission of liquidity shocks across investors and securities," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 233-254, July.
    10. Denuit, Michel & Lambert, Philippe, 2005. "Constraints on concordance measures in bivariate discrete data," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 40-57, March.
    11. Michaely, Roni & Vila, Jean-Luc, 1996. "Trading Volume with Private Valuation: Evidence from the Ex-dividend Day," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 471-509.
    12. Roman Liesenfeld & Ingmar Nolte & Winfried Pohlmeier, 2006. "Modelling financial transaction price movements: a dynamic integer count data model," Empirical Economics, Springer, vol. 30(4), pages 795-825, January.
    13. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    14. Karpoff, Jonathan M, 1986. " A Theory of Trading Volume," Journal of Finance, American Finance Association, vol. 41(5), pages 1069-1087, December.
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    More about this item


    Liquidity; Copula Functions; Trading Process; Decimalization; Metropolized-Independence Sampler;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • F30 - International Economics - - International Finance - - - General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General


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