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Liquidity adjusted value-at-risk based on the components of the bid-ask spread

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  • Timotheos Angelidis
  • Alexandros Benos

Abstract

This paper proposes a method of calculating a Liquidity Adjusted Value-at-Risk (L-VaR) measure. Traditional VaR approaches assume perfect markets, where an investor can buy or sell any amount of stock without causing a significant price change. Such a hypothesis is seldom verified in practice, especially in emerging markets, consequently underestimating the VaR risk measure. An attempt is made to remedy this shortcoming by first estimating the bid-ask spread components in order to calculate accurately both the endogeneous and the exogenous liquidity risk. Under this framework, the liquidation price of a position will not be the spread midpoint, but at most the bid price. The Madhavan et al. (1997) model is extended by incorporating the traded volume and find that liquidity risk, for an emerging stock market, displays an inverse U-shape pattern throughout the day. For the high-priced, high-capitalization stocks of the Athens Stock Exchange, it represents 3.40% of total market risk, while for the low capitalization ones, it is even higher at 11%. VaR measures are then adjusted for such spread variation since, neglecting such effect, leads so serious failure of VaR backtesting.

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  • 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.
    • Handle: RePEc:taf:apfiec:v:16:y:2006:i:11:p:835-851
    DOI: 10.1080/09603100500426440
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    1. Ramos, Henrique Pinto & Righi, Marcelo Brutti, 2020. "Liquidity, implied volatility and tail risk: A comparison of liquidity measures," International Review of Financial Analysis, Elsevier, vol. 69(C).
    2. 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.
    3. Timotheos Angelidis & Alexandros Benos, 2009. "The Components of the Bid‐Ask Spread: the Case of the Athens Stock Exchange," European Financial Management, European Financial Management Association, vol. 15(1), pages 112-144, January.
    4. Xavier Brouty & Matthieu Garcin & Hugo Roccaro, 2024. "Estimation of bid-ask spreads in the presence of serial dependence," Papers 2407.17401, arXiv.org, revised Jan 2025.
    5. Al Janabi, Mazin A.M., 2012. "Optimal commodity asset allocation with a coherent market risk modeling," Review of Financial Economics, Elsevier, vol. 21(3), pages 131-140.
    6. Al Janabi, Mazin A.M. & Arreola Hernandez, Jose & Berger, Theo & Nguyen, Duc Khuong, 2017. "Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1121-1131.
    7. 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.
    8. 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).
    9. Petr Strnad, 2009. "Market liquidity risk and its incorporation into value at risk [Riziko tržní likvidity a jeho zohlednění v ukazateli value at risk]," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2009(2), pages 21-37.
    10. Mariano González-Sánchez & Eva M. Ibáñez Jiménez & Ana I. Segovia San Juan, 2021. "Market and Liquidity Risks Using Transaction-by-Transaction Information," Mathematics, MDPI, vol. 9(14), pages 1-14, July.
    11. 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.
    12. Alexander, Carol & Sheedy, Elizabeth, 2008. "Developing a stress testing framework based on market risk models," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2220-2236, October.
    13. Damiano Brigo & Mirela Predescu & Agostino Capponi, 2010. "Credit Default Swaps Liquidity modeling: A survey," Papers 1003.0889, arXiv.org, revised Mar 2010.
    14. Berlinger, Edina & Dömötör, Barbara & Illés, Ferenc, 2019. "Anti-cyclical versus risk-sensitive margin strategies in central clearing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 117-131.
    15. Al Janabi, Mazin A.M. & Ferrer, Román & Shahzad, Syed Jawad Hussain, 2019. "Liquidity-adjusted value-at-risk optimization of a multi-asset portfolio using a vine copula approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    16. Y. C. Su & H. C. Huang & Y. J. Lin, 2011. "GJR-GARCH model in value-at-risk of financial holdings," Applied Financial Economics, Taylor & Francis Journals, vol. 21(24), pages 1819-1829, December.
    17. Sean A. Anthonisz & Tālis J. Putniņš, 2017. "Asset Pricing with Downside Liquidity Risks," Management Science, INFORMS, vol. 63(8), pages 2549-2572, August.
    18. Al Janabi, Mazin A. M., 2009. "Asset Market Liquidity Risk Management: A Generalized Theoretical Modeling Approach for Trading and Fund Management Portfolios," MPRA Paper 19498, University Library of Munich, Germany.
    19. Manuel Ammann & Stephan Markus Kessler, 2009. "Intraday characteristics of stock price crashes," Applied Financial Economics, Taylor & Francis Journals, vol. 19(15), pages 1239-1255.
    20. 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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