IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v36y2014icp332-340.html
   My bibliography  Save this article

The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range

Author

Listed:
  • Todorova, Neda
  • Souček, Michael

Abstract

Intraday data of 26 German stocks are used to investigate whether the information contained in trading volume and number of trades as well as in various specifications of overnight returns can improve one-step-ahead volatility forecasts. For this purpose, a HAR model of the realized range adjusted for discrete trading is augmented by each of these variables and compared with the model's default form. The results show that the considered liquidity measures lead to very modest improvements in forecasting performance. The overnight returns exhibit some in-sample forecasting power. However, the accuracy improvement of out-of-sample forecasts is unequivocally non-significant.

Suggested Citation

  • Todorova, Neda & Souček, Michael, 2014. "The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range," Economic Modelling, Elsevier, vol. 36(C), pages 332-340.
    • Handle: RePEc:eee:ecmode:v:36:y:2014:i:c:p:332-340
    DOI: 10.1016/j.econmod.2013.10.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S026499931300429X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2013.10.003?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. Chan, Kalok & Fong, Wai-Ming, 2000. "Trade size, order imbalance, and the volatility-volume relation," Journal of Financial Economics, Elsevier, vol. 57(2), pages 247-273, August.
    2. Aït-Sahalia, Yacine & Mancini, Loriano, 2008. "Out of sample forecasts of quadratic variation," Journal of Econometrics, Elsevier, vol. 147(1), pages 17-33, November.
    3. Fuertes, Ana-Maria & Izzeldin, Marwan & Kalotychou, Elena, 2009. "On forecasting daily stock volatility: The role of intraday information and market conditions," International Journal of Forecasting, Elsevier, vol. 25(2), pages 259-281.
    4. Martens, Martin & van Dijk, Dick, 2007. "Measuring volatility with the realized range," Journal of Econometrics, Elsevier, vol. 138(1), pages 181-207, May.
    5. Gallo, Giampiero M, 2001. "Modelling the Impact of Overnight Surprises on Intra-Daily Volatility," Australian Economic Papers, Wiley Blackwell, vol. 40(4), pages 567-580, December.
    6. Giampiero Gallo & Barbara Pacini, 2000. "The effects of trading activity on market volatility," The European Journal of Finance, Taylor & Francis Journals, vol. 6(2), pages 163-175.
    7. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    8. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    9. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    10. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    11. Jones, Charles M & Kaul, Gautam & Lipson, Marc L, 1994. "Transactions, Volume, and Volatility," Review of Financial Studies, Society for Financial Studies, vol. 7(4), pages 631-651.
    12. Giampiero M. Gallo, 2001. "Modelling the Impact of Overnight Surprises on Intra‐daily Volatility," Australian Economic Papers, Wiley Blackwell, vol. 40(4), pages 567-580, December.
    13. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    14. Joshy Jacob & Vipul, 2008. "Estimation and forecasting of stock volatility with range‐based estimators," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(6), pages 561-581, June.
    15. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
    16. Neda Todorova & Sven Husmann, 2012. "A comparative study of range‐based stock return volatility estimators for the German market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(6), pages 560-586, June.
    17. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    18. Tseng-Chan Tseng & Hung-Cheng Lai & Cha-Fei Lin, 2012. "The impact of overnight returns on realized volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 22(5), pages 357-364, March.
    19. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    20. R. Glen Donaldson & Mark J. Kamstra, 2005. "Volatility Forecasts, Trading Volume, And The Arch Versus Option‐Implied Volatility Trade‐Off," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 28(4), pages 519-538, December.
    21. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    22. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    23. Fleming, Jeff & Kirby, Chris, 2011. "Long memory in volatility and trading volume," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1714-1726, July.
    24. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    25. 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.
    26. Bollerslev, Tim & Jubinski, Dan, 1999. "Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 9-21, January.
    27. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March.
    28. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    29. Darrat, Ali F. & Rahman, Shafiqur & Zhong, Maosen, 2003. "Intraday trading volume and return volatility of the DJIA stocks: A note," Journal of Banking & Finance, Elsevier, vol. 27(10), pages 2035-2043, October.
    30. Peter Reinhard Hansen & Asger Lunde, 2005. "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 525-554.
    31. Chen, Gong-meng & Firth, Michael & Rui, Oliver M, 2001. "The Dynamic Relation between Stock Returns, Trading Volume, and Volatility," The Financial Review, Eastern Finance Association, vol. 36(3), pages 153-173, August.
    32. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    33. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
    34. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, vol. 67(3), pages 473-509, March.
    35. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
    36. Neda Todorova, 2012. "Volatility estimators based on daily price ranges versus the realized range," Applied Financial Economics, Taylor & Francis Journals, vol. 22(3), pages 215-229, February.
    37. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.
    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. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    2. Laurence E. Blose & Vijay Gondhalekar & Alan Kort, 2018. "Overnight versus day returns in gold and gold related assets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(3), pages 526-549, July.
    3. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    4. Ma, Feng & Liu, Jing & Huang, Dengshi & Chen, Wang, 2017. "Forecasting the oil futures price volatility: A new approach," Economic Modelling, Elsevier, vol. 64(C), pages 560-566.
    5. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    6. Mehmet Balcilar & Elie Bouri & Rangan Gupta & David Roubaud, 2016. "Can Volume Predict Bitcoin Returns and Volatility? A Nonparametric Causality-in-Quantiles Approach," Working Papers 201662, University of Pretoria, Department of Economics.
    7. Gkillas, Konstantinos & Konstantatos, Christoforos & Floros, Christos & Tsagkanos, Athanasios, 2021. "Realized volatility spillovers between US spot and futures during ECB news: Evidence from the European sovereign debt crisis," International Review of Financial Analysis, Elsevier, vol. 74(C).
    8. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    9. Kao, Yu-Sheng & Zhao, Kai & Chuang, Hwei-Lin & Ku, Yu-Cheng, 2024. "The asymmetric relationships between the Bitcoin futures’ return, volatility, and trading volume," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 524-542.
    10. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    11. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2016. "Forecasting stock volatility using after-hour information: Evidence from the Australian Stock Exchange," Economic Modelling, Elsevier, vol. 52(PB), pages 592-608.
    12. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.
    13. Nasiri, S. & Bektas, E. & Jafari, G.R., 2018. "The impact of trading volume on the stock market credibility: Bohmian quantum potential approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1104-1112.

    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. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    2. repec:uts:finphd:39 is not listed on IDEAS
    3. repec:uts:finphd:38 is not listed on IDEAS
    4. Fei Su, 2018. "Essays on Price Discovery and Volatility Dynamics in the Foreign Exchange Market," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2018.
    5. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    6. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    7. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    8. Rossi, Eduardo & Santucci de Magistris, Paolo, 2013. "Long memory and tail dependence in trading volume and volatility," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 94-112.
    9. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    10. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    11. Dimos Kambouroudis & David McMillan & Katerina Tsakou, 2019. "Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility," Working Papers 2019-03, Swansea University, School of Management.
    12. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    13. Maheu, John M. & McCurdy, Thomas H., 2011. "Do high-frequency measures of volatility improve forecasts of return distributions?," Journal of Econometrics, Elsevier, vol. 160(1), pages 69-76, January.
    14. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.
    15. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
    16. Ma, Chaoqun & Mi, Xianhua & Cai, Zongwu, 2020. "Nonlinear and time-varying risk premia," China Economic Review, Elsevier, vol. 62(C).
    17. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.
    18. Tseng-Chan Tseng & Hung-Cheng Lai & Cha-Fei Lin, 2012. "The impact of overnight returns on realized volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 22(5), pages 357-364, March.
    19. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
    20. Dimitrios P. Louzis & Spyros Xanthopoulos-Sisinis & Apostolos P. Refenes, 2012. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Applied Economics, Taylor & Francis Journals, vol. 44(27), pages 3533-3550, September.
    21. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
    22. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," OFRC Working Papers Series 2005fe08, Oxford Financial Research Centre.

    More about this item

    Keywords

    Volatility forecasting; HAR model; Realized range; Overnight returns; Trading volume; Number of trades;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:ecmode:v:36:y:2014:i:c:p:332-340. 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/inca/30411 .

    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.