IDEAS home Printed from https://ideas.repec.org/a/ejn/ejbmjr/v5y2017i1p55-69.html
   My bibliography  Save this article

Liquidity Adjusted Value At Risk: Integrating The Uncertainty In Depth And Tightness

Author

Listed:
  • Levent C. Uslu

    (Yeditepe University, Turkey)

  • Burak Evre

    (Riskturk Risk Software Technologies, Turkey)

Abstract

Efficient market risk management should also focus on the market liquidity risk, which is generally ignored by the conventional Value at Risk Metrics. We propose two alternative parametric methods to existing studies for the estimation of Liquidity Adjusted Value at Risk (LVaR). The first model is based on the volatility dynamics of VNET (Engle and Lange, 2001) whereas the second model also incorporates the first two moments of the tightness dimension to the latter, as measured by relative weighted bid-ask spreads. Considering a portfolio with different underlying volatility assumptions (EWMA, GARCH-CCC, GARCH-DCC), validation results indicate that both parametric LVaR approaches are strong alternatives to tightness based LVaR models and strictly superior to conventional VaR models, with respect to performance related to regulatory compliance, statistical coverage and overall relative cost of liquidity vs. loss size on violation days.

Suggested Citation

  • 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.
    • Handle: RePEc:ejn:ejbmjr:v:5:y:2017:i:1:p:55-69
    as

    Download full text from publisher

    File URL: https://eurasianpublications.com/wp-content/uploads/2021/02/EJBM-5.1.6.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    2. Engle, Robert F. & Lange, Joe, 2001. "Predicting VNET: A model of the dynamics of market depth," Journal of Financial Markets, Elsevier, vol. 4(2), pages 113-142, April.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. 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.).
    5. Naes, Randi & Skjeltorp, Johannes A., 2006. "Order book characteristics and the volume-volatility relation: Empirical evidence from a limit order market," Journal of Financial Markets, Elsevier, vol. 9(4), pages 408-432, November.
    6. Stange, Sebastian & Kaserer, Christoph, 2008. "The impact of order size on stock liquidity: a representative study," CEFS Working Paper Series 2008-09, Technische Universität München (TUM), Center for Entrepreneurial and Financial Studies (CEFS).
    7. 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.
    8. Emna Rouetbi & Chokri Mamoghli, 2014. "Measuring Liquidity in an Emerging Market: The Tunis Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 920-929.
    9. 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.
    10. 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.
    11. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    12. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    13. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christian Maier & Oliver Seger, 2017. "Dynamic Hybrids Under Solvency Ii: Risk Analysis And Modification Possibilities," Eurasian Journal of Social Sciences, Eurasian Publications, vol. 5(2), pages 12-17.
    2. Jorge Mongay Hurtado, 2018. "Customer Concentration Versus Fragmentation And Its Implications In Corporate Risk," Eurasian Journal of Business and Management, Eurasian Publications, vol. 6(1), pages 1-6.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rita Pimentel & Morten Risstad & Sjur Westgaard, 2022. "Predicting interest rate distributions using PCA & quantile regression," Digital Finance, Springer, vol. 4(4), pages 291-311, December.
    2. da Veiga, Bernardo & Chan, Felix & McAleer, Michael, 2008. "Evaluating the impact of market reforms on Value-at-Risk forecasts of Chinese A and B shares," Pacific-Basin Finance Journal, Elsevier, vol. 16(4), pages 453-475, September.
    3. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    4. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    5. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    6. esposito, francesco paolo & cummins, mark, 2015. "Multiple hypothesis testing of market risk forecasting models," MPRA Paper 64986, University Library of Munich, Germany.
    7. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    8. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
    9. Szubzda Filip & Chlebus Marcin, 2019. "Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions," Central European Economic Journal, Sciendo, vol. 6(53), pages 70-85, January.
    10. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    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. Cathy W. S. Chen & Hong Than-Thi & Manabu Asai, 2021. "On a Bivariate Hysteretic AR-GARCH Model with Conditional Asymmetry in Correlations," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 413-433, August.
    13. Chrétien, Stéphane & Coggins, Frank, 2010. "Performance and conservatism of monthly FHS VaR: An international investigation," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 323-333, December.
    14. Wu, Qi & Yan, Xing, 2019. "Capturing deep tail risk via sequential learning of quantile dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    15. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    16. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    17. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2021. "Bayesian estimation for a semiparametric nonlinear volatility model," Economic Modelling, Elsevier, vol. 98(C), pages 361-370.
    18. Van Cauwenberge, Annelies & Vancauteren, Mark & Braekers, Roel & Vandemaele, Sigrid, 2019. "International trade, foreign direct investments, and firms’ systemic risk : Evidence from the Netherlands," Economic Modelling, Elsevier, vol. 81(C), pages 361-386.
    19. Donggyu Kim & Minseog Oh & Yazhen Wang, 2022. "Conditional quantile analysis for realized GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 640-665, July.
    20. Makushkin, Mikhail & Lapshin, Victor, 2020. "Modelling tail dependencies between Russian and foreign stock markets: Application for market risk valuation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 30-52.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ejn:ejbmjr:v:5:y:2017:i:1:p:55-69. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Esra Barakli (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.