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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.

Suggested Citation

  • GIOT, Pierre & GRAMMIG, Joachim, 2002. "How large is liquidity risk in an automated auction market ?," CORE Discussion Papers 2002054, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2002054
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Levent C. Uslu & Burak Evren, 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.
    7. Holmberg, Ulf, 2012. "Essays on Credit Markets and Banking," Umeå Economic Studies 840, Umeå University, Department of Economics.
    8. 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.
    9. 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.

    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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