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Assessing liquidity‐adjusted risk forecasts

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  • Theo Berger
  • Christina Uffmann

Abstract

In this paper, we provide a thorough study on the relevance of liquidity‐adjusted value‐at‐risk (LVaR) and expected shortfall (LES) forecasts. We measure additional liquidity of an asset via the difference between its respective bid and ask prices and we assess the non‐normality of bid–ask spreads, especially in turbulent market times. The empirical assessment comprises German stocks in both calm and turmoil market times, and our results provide evidence that liquidity risk turns out to be crucial for the quality of regulatory risk assessment in turmoil market times. We find that a Cornish–Fisher approximation describes a sensible choice for LVaR forecasts whereas an extreme value approach results in adequate LES forecasts.

Suggested Citation

  • Theo Berger & Christina Uffmann, 2021. "Assessing liquidity‐adjusted risk forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1179-1189, November.
  • Handle: RePEc:wly:jforec:v:40:y:2021:i:7:p:1179-1189
    DOI: 10.1002/for.2758
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    References listed on IDEAS

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    1. Bertrand Candelon & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2011. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 314-343, Spring.
    2. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    3. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy & Didier Maillard, 2019. "Computation of the corrected Cornish–Fisher expansion using the response surface methodology: application to VaR and CVaR," Annals of Operations Research, Springer, vol. 281(1), pages 423-453, October.
    4. 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).
    5. Chui-Chun Tsai & Tsun-Siou Lee, 2017. "Liquidity-Adjusted Value-at-Risk for TWSE Leverage/ Inverse ETFs: A Hellinger Distance Measure Research," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 13(1), pages 53-81, February.
    6. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    7. 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.
    8. Cihan Aktas & Orcan Cortuk & Suat Teker & Burcu Deniz Yildirim, 2012. "Measurement of Liquidity-Adjusted Market Risk by VaR and Expected Shortfall: Evidence from Turkish Banks," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(5), pages 1-8.
    9. Pierre Giot & Joachim Grammig, 2006. "How large is liquidity risk in an automated auction market?," Empirical Economics, Springer, vol. 30(4), pages 867-887, January.
    10. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    11. Sebastian Stange & Christoph Kaserer, 2011. "The Impact of Liquidity Risk: A Fresh Look," International Review of Finance, International Review of Finance Ltd., vol. 11(3), pages 269-301, September.
    12. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    13. Lakshithe Wagalath & Jorge Zubelli, 2018. "A Liquidation Risk Adjustment For Value At Risk And Expected Shortfall," Post-Print hal-02572794, HAL.
    14. Lakshithe Wagalath & Jorge P. Zubelli, 2018. "A Liquidation Risk Adjustment For Value At Risk And Expected Shortfall," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 1-21, May.
    15. repec:cii:cepiei:2013-q1-133-6 is not listed on IDEAS
    16. Theo Berger, 2013. "Forecasting value-at-risk using time varying copulas and EVT return distributions," International Economics, CEPII research center, issue 133, pages 93-106.
    17. Berger, T. & Missong, M., 2014. "Financial crisis, Value-at-Risk forecasts and the puzzle of dependency modeling," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 33-38.
    18. 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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    1. 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.

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