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Why and how to integrate liquidity risk into a VaR-framework

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  • Stange, Sebastian
  • Kaserer, Christoph

Abstract

We integrate liquidity risk measured by the weighted spread into a Value-at-Risk (VaR) framework. The weighted spread measure extracts liquidity costs by order size from the limit order book. We show that it is precise from a risk perspective in a wide range of clearly defined situations. Using a unique, representative data set provided by Deutsche Boerse AG, we find liquidity risk to increase traditionally-measured price risk by over 25%, even at standard 10-day horizons and for liquid DAX stocks. We also show that the common approach of simply adding liquidity risk to price risk substantially overestimates total risk because correlation between liquidity and price is neglected. Our results are robust with respect to changes in risk measure, to sample periods and to effects of portfolio diversification.

Suggested Citation

  • Stange, Sebastian & Kaserer, Christoph, 2008. "Why and how to integrate liquidity risk into a VaR-framework," CEFS Working Paper Series 2008-10, Technische Universität München (TUM), Center for Entrepreneurial and Financial Studies (CEFS).
  • Handle: RePEc:zbw:cefswp:200810
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    References listed on IDEAS

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    1. Anil Bangia & Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Modeling Liquidity Risk With Implications for Traditional Market Risk Measurement and Management," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-062, New York University, Leonard N. Stern School of Business-.
    2. Carlo Acerbi & Giacomo Scandolo, 2008. "Liquidity risk theory and coherent measures of risk," Quantitative Finance, Taylor & Francis Journals, vol. 8(7), pages 681-692.
    3. Domowitz, Ian & Hansch, Oliver & Wang, Xiaoxin, 2005. "Liquidity commonality and return co-movement," Journal of Financial Markets, Elsevier, vol. 8(4), pages 351-376, November.
    4. Timotheos Angelidis & Alexandros Benos, 2006. "Liquidity adjusted value-at-risk based on the components of the bid-ask spread," Applied Financial Economics, Taylor & Francis Journals, vol. 16(11), pages 835-851.
    5. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    6. Robert Almgren, 2003. "Optimal execution with nonlinear impact functions and trading-enhanced risk," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(1), pages 1-18.
    7. Hisata, Yoshifumi & Yamai, Yasuhiro, 2000. "Research toward the Practical Application of Liquidity Risk Evaluation Methods," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 83-127, December.
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    Cited by:

    1. Damiano Brigo & Mirela Predescu & Agostino Capponi, 2010. "Credit Default Swaps Liquidity modeling: A survey," Papers 1003.0889, arXiv.org, revised Mar 2010.

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    More about this item

    Keywords

    asset liquidity; price impact; weighted spread; Xetra Liquidity Measure (XLM); Value-at-Risk; market liquidity risk;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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