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How large is liquidity risk in an automated auction market?

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  • GIOT, Pierre
  • GRAMMIG, Joachim

Abstract

We introduce a new empirical methodology that takes account of liquidity risk in a Value-at-Risk framework, and quantify liquidity risk premiums for portfolios and individual stocks traded on the automated auction market Xetra which operates at various European exchanges. When constructing liquidity risk measures we allow for the potential price impact incurred by the liquidation of a portfolio. We study the sensitivity of liquidity risk towards portfolio size and VaR time horizon, and interpret its diurnal variation in the light of market microstructure theory.
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Suggested Citation

  • GIOT, Pierre & GRAMMIG, Joachim, 2006. "How large is liquidity risk in an automated auction market?," LIDAM Reprints CORE 1846, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1846
    DOI: 10.1007/s00181-005-0003-z
    Note: In : Empirical Economics, 30, 867-887, 2006
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    1. Helena, BELTRAN & Alain, DURRE & Pierre, GIOT, 2004. "Volatility regimes and the provisions of liquidity in order book markets," Discussion Papers (ECON - Département des Sciences Economiques) 2005015, Université catholique de Louvain, Département des Sciences Economiques.
    2. Anil Bangia & Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Modeling Liquidity Risk, With Implications for Traditional Market Risk Measurement and Management," Center for Financial Institutions Working Papers 99-06, Wharton School Center for Financial Institutions, University of Pennsylvania.
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    5. Biais, Bruno & Hillion, Pierre & Spatt, Chester, 1995. "An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse," Journal of Finance, American Finance Association, vol. 50(5), pages 1655-1689, December.
    6. Gourieroux, Christian & Jasiak, Joanna & Le Fol, Gaelle, 1999. "Intra-day market activity," Journal of Financial Markets, Elsevier, vol. 2(3), pages 193-226, August.
    7. Ajay Subramanian & Robert A. Jarrow, 2001. "The Liquidity Discount," Mathematical Finance, Wiley Blackwell, vol. 11(4), pages 447-474, October.
    8. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    9. GIOT, Pierre, 1999. "Time transformations, intraday data and volatility models," LIDAM Discussion Papers CORE 1999044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    11. Magdalena E. Sokalska & Ananda Chanda & Robert F. Engle, 2005. "High Frequency Multiplicative Component Garch," Computing in Economics and Finance 2005 409, Society for Computational Economics.
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    Citations

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    Cited by:

    1. Großmaß Lidan, 2014. "Liquidity and the Value at Risk," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(5), pages 572-602, October.
    2. Dionne, Georges & Pacurar, Maria & Zhou, Xiaozhou, 2015. "Liquidity-adjusted Intraday Value at Risk modeling and risk management: An application to data from Deutsche Börse," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 202-219.
    3. Héléna Beltran-Lopez & Pierre Giot & Joachim Grammig, 2009. "Commonalities in the order book," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(3), pages 209-242, September.
    4. Dionne, Georges & Zhou, Xiaozhou, 2016. "The Dynamics of Ex-ante High-Frequency Liquidity: An Empirical Analysis," Working Papers 15-5, HEC Montreal, Canada Research Chair in Risk Management.
    5. Lönnbark, Carl & Holmberg, Ulf & Brännäs, Kurt, 2011. "Value at Risk and Expected Shortfall for large portfolios," Finance Research Letters, Elsevier, vol. 8(2), pages 59-68, June.
    6. Ernst, Cornelia & Stange, Sebastian & Kaserer, Christoph, 2012. "Measuring market liquidity risk - which model works best?," Journal of Financial Transformation, Capco Institute, vol. 35, pages 133-146.
    7. Theo Berger & Christina Uffmann, 2021. "Assessing liquidity‐adjusted risk forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1179-1189, November.
    8. Dionne, Georges & Duchesne, Pierre & Pacurar, Maria, 2009. "Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 777-792, December.
    9. Peter Gomber & Uwe Schweickert & Erik Theissen, 2015. "Liquidity Dynamics in an Electronic Open Limit Order Book: an Event Study Approach," European Financial Management, European Financial Management Association, vol. 21(1), pages 52-78, January.
    10. Wang, Keli & Liu, Xiaoquan & Ye, Wuyi, 2023. "Intraday VaR: A copula-based approach," Journal of Empirical Finance, Elsevier, vol. 74(C).
    11. Georges Dionne & Xiaozhou Zhou, 2020. "The dynamics of ex-ante weighted spread: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 20(4), pages 593-617, April.
    12. Levent C. Uslu & Burak Evre, 2017. "Liquidity Adjusted Value At Risk: Integrating The Uncertainty In Depth And Tightness," Eurasian Journal of Business and Management, Eurasian Publications, vol. 5(1), pages 55-69.
    13. Holmberg, Ulf, 2012. "Essays on Credit Markets and Banking," Umeå Economic Studies 840, Umeå University, Department of Economics.
    14. Rouetbi Emnal & Mamoghli Chokri, 2014. "Measuring Liquidity Risk in an Emerging Market: Liquidity Adjusted Value at Risk Approach for High Frequency Data," International Journal of Economics and Financial Issues, Econjournals, vol. 4(1), pages 40-53.

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    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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