IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v189y2015i2p437-446.html
   My bibliography  Save this article

Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach

Author

Listed:
  • Liu, Shouwei
  • Tse, Yiu-Kuen

Abstract

We propose to compute the Intraday Value-at-Risk (IVaR) for stocks using real-time transaction data. Tick-by-tick data filtered by price duration are modeled using a two-state asymmetric autoregressive conditional duration (AACD) model, and the IVaR is calculated using Monte Carlo simulation based on the estimated AACD model. Backtesting results for the New York Stock Exchange (NYSE) show that the IVaR calculated using the AACD method outperforms those using the Dionne et al. (2009) and Giot (2005) methods.

Suggested Citation

  • Liu, Shouwei & Tse, Yiu-Kuen, 2015. "Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach," Journal of Econometrics, Elsevier, vol. 189(2), pages 437-446.
    • Handle: RePEc:eee:econom:v:189:y:2015:i:2:p:437-446
    DOI: 10.1016/j.jeconom.2015.03.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030440761500113X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2015.03.035?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. Luc Bauwens & Pierre Giot, 2003. "Asymmetric ACD models: Introducing price information in ACD models," Empirical Economics, Springer, vol. 28(4), pages 709-731, November.
    4. Pierre Giot, 2005. "Market risk models for intraday data," The European Journal of Finance, Taylor & Francis Journals, vol. 11(4), pages 309-324.
    5. 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.).
    6. Bauwens, Luc & Giot, Pierre & Grammig, Joachim & Veredas, David, 2004. "A comparison of financial duration models via density forecasts," International Journal of Forecasting, Elsevier, vol. 20(4), pages 589-609.
    7. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    8. Laura Coroneo & David Veredas, 2012. "A simple two-component model for the distribution of intraday returns," The European Journal of Finance, Taylor & Francis Journals, vol. 18(9), pages 775-797, October.
    9. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
    10. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
    11. Tse, Yiu-Kuen & Dong, Yingjie, 2014. "Intraday periodicity adjustments of transaction duration and their effects on high-frequency volatility estimation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 352-361.
    12. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    13. Yiu-kuen Tse & Thomas Tao Yang, 2012. "Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 533-545, April.
    14. Wu, Zhengxiao, 2012. "On the intraday periodicity duration adjustment of high-frequency data," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 282-291.
    15. Russell, Jeffrey R. & Engle, Robert F., 2005. "A Discrete-State Continuous-Time Model of Financial Transactions Prices and Times: The Autoregressive Conditional Multinomial-Autoregressive Conditional Duration Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 166-180, April.
    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. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    2. Ling, Shiqing & McAleer, Michael & Tong, Howell, 2015. "Frontiers in Time Series and Financial Econometrics: An overview," Journal of Econometrics, Elsevier, vol. 189(2), pages 245-250.
    3. Kuck, Konstantin & Maderitsch, Robert, 2019. "Intra-day dynamics of exchange rates: New evidence from quantile regression," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 247-257.
    4. Landsman, Zinoviy & Makov, Udi & Shushi, Tomer, 2016. "Tail conditional moments for elliptical and log-elliptical distributions," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 179-188.
    5. Tobias Eckernkemper & Bastian Gribisch, 2021. "Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 883-910, August.
    6. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    7. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    8. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    9. Ling, S. & McAleer, M.J. & Tong, H., 2015. "Frontiers in Time Series and Financial Econometrics," Econometric Institute Research Papers EI 2015-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    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. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
    2. Maria Pacurar, 2008. "Autoregressive Conditional Duration Models In Finance: A Survey Of The Theoretical And Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 711-751, September.
    3. Xiufeng Yan, 2021. "Autoregressive conditional duration modelling of high frequency data," Papers 2111.02300, arXiv.org.
    4. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    5. 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.
    6. Xiufeng Yan, 2021. "Multiplicative Component GARCH Model of Intraday Volatility," Papers 2111.02376, arXiv.org.
    7. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    8. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    9. Weiß, Gregor N.F. & Supper, Hendrik, 2013. "Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3334-3350.
    10. Tse, Yiu-Kuen & Dong, Yingjie, 2014. "Intraday periodicity adjustments of transaction duration and their effects on high-frequency volatility estimation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 352-361.
    11. Huiling Yuan & Yulei Sun & Lu Xu & Yong Zhou & Xiangyu Cui, 2022. "A new volatility model: GQARCH‐ItÔ model," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 345-370, May.
    12. Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
    13. Katarzyna Bien-Barkowska, 2011. "Distribution Choice for the Asymmetric ACD Models," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 55-72.
    14. Charlie X. Cai & Qi Zhang, 2016. "High†Frequency Exchange Rate Forecasting," European Financial Management, European Financial Management Association, vol. 22(1), pages 120-141, January.
    15. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2014. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(1), pages 89-121.
    16. Stanislav Anatolyev & Dmitry Shakin, 2006. "Trade intensity in the Russian stock market:dynamics, distribution and determinants," Working Papers w0070, Center for Economic and Financial Research (CEFIR).
    17. Yiing Fei Tan & Kok Haur Ng & You Beng Koh & Shelton Peiris, 2022. "Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    18. Elena-Ivona Dumitrescu & Christophe Hurlin & Vinson Pham, 2012. "Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests," Finance, Presses universitaires de Grenoble, vol. 33(1), pages 79-112.
    19. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
    20. Vasileios Siakoulis & Ioannis Venetis, 2015. "On inter-arrival times of bond market extreme events. An application to seven European markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(4), pages 717-741, October.

    More about this item

    Keywords

    High-frequency transaction data; Market microstructure noise; Asymmetric autoregressive conditional duration model; Intraday Value-at-Risk; Backtesting;
    All these keywords.

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    Statistics

    Access and download statistics

    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:eee:econom:v:189:y:2015:i:2:p:437-446. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    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.