IDEAS home Printed from https://ideas.repec.org/a/fau/fauart/v66y2016i5p405-425.html
   My bibliography  Save this article

Impact of US Macroeconomic News Announcements on Intraday Causalities on Selected European Stock Markets

Author

Listed:
  • Henryk Gurgul

    (AGH University of Science and Technology, Krakow)

  • Lukaz Lach

    (AGH University of Science and Technology, Krakow)

  • Tomasz Wojtowicz

    (AGH University of Science and Technology, Krakow)

Abstract

In this paper we examine the impact of US macroeconomic news announcements on the relationships between returns, volatility and turnover on three European stock markets operating in Frankfurt, Vienna and Warsaw. The empirical analysis in periods with and without important publicly available macroeconomic news is based on intraday data of the main indices of these stock markets, namely DAX, ATX and WIG20. Announcements of important publicly available macroeconomic news essentially increase the number of causal relationships on the markets and between them. Granger causality tests confirm the dominant role of the Frankfurt Stock Exchange. Causality running from DAX returns to returns of ATX and WIG20 is statistically significant irrespective of the time of day and the presence of important macroeconomic news announcements. The only visible feedback runs between WIG20- and DAX-related variables. We also find that most of the causal relationships between the stock exchanges in Warsaw and Vienna are implied by data from the stock exchange in Frankfurt.

Suggested Citation

  • Henryk Gurgul & Lukaz Lach & Tomasz Wojtowicz, 2016. "Impact of US Macroeconomic News Announcements on Intraday Causalities on Selected European Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 405-425, October.
  • Handle: RePEc:fau:fauart:v:66:y:2016:i:5:p:405-425
    as

    Download full text from publisher

    File URL: http://journal.fsv.cuni.cz/storage/1368_gurgul.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James C. Luu & Martin Martens, 2003. "Testing the mixture‐of‐distributions hypothesis using “realized” volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 23(7), pages 661-679, July.
    2. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
    3. Pritamani, Mahesh & Singal, Vijay, 2001. "Return predictability following large price changes and information releases," Journal of Banking & Finance, Elsevier, vol. 25(4), pages 631-656, April.
    4. Henryk Gurgul & Tomasz Wójtowicz, 2014. "The impact of US macroeconomic news on the Polish stock market," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(4), pages 795-817, December.
    5. Robinson, Peter M. & Yajima, Yoshihiro, 2002. "Determination of cointegrating rank in fractional systems," Journal of Econometrics, Elsevier, vol. 106(2), pages 217-241, February.
    6. Bauwens, Luc & Ben Omrane, Walid & Giot, Pierre, 2005. "News announcements, market activity and volatility in the euro/dollar foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1108-1125, November.
    7. Darrat, Ali F. & Zhong, Maosen & Cheng, Louis T.W., 2007. "Intraday volume and volatility relations with and without public news," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2711-2729, September.
    8. Henryk Gurgul & Łukasz Lach, 2009. "Linear versus nonlinear causality for DAX companies," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 19(3), pages 27-46.
    9. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    10. Henryk GURGUL & Tomasz WÓJTOWICZ, 2006. "Long Memory on the German Stock Exchange," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 56(09-10), pages 447-468, September.
    11. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    12. Jan Hanousek & Evžen Kočenda, 2011. "Foreign News and Spillovers in Emerging European Stock Markets," Review of International Economics, Wiley Blackwell, vol. 19(1), pages 170-188, February.
    13. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    14. Fleming, Jeff & Kirby, Chris, 2011. "Long memory in volatility and trading volume," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1714-1726, July.
    15. Kari Harju & Syed Mujahid Hussain, 2011. "Intraday Seasonalities and Macroeconomic News Announcements," European Financial Management, European Financial Management Association, vol. 17(2), pages 367-390, March.
    16. Lobato, Ignacio N & Velasco, Carlos, 2000. "Long Memory in Stock-Market Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 410-427, October.
    17. 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.
    18. Lee, Bong-Soo & Rui, Oliver M., 2002. "The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence," Journal of Banking & Finance, Elsevier, vol. 26(1), pages 51-78, January.
    19. 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.
    20. 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.
    21. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    22. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Vega, Clara, 2007. "Real-time price discovery in global stock, bond and foreign exchange markets," Journal of International Economics, Elsevier, vol. 73(2), pages 251-277, November.
    23. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    24. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    25. Harris, Lawrence, 1986. "A transaction data study of weekly and intradaily patterns in stock returns," Journal of Financial Economics, Elsevier, vol. 16(1), pages 99-117, May.
    26. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
    27. Copeland, Thomas E, 1976. "A Model of Asset Trading under the Assumption of Sequential Information Arrival," Journal of Finance, American Finance Association, vol. 31(4), pages 1149-1168, September.
    28. Nikkinen, Jussi & Omran, Mohammed & Sahlstrom, Petri & Aijo, Janne, 2006. "Global stock market reactions to scheduled U.S. macroeconomic news announcements," Global Finance Journal, Elsevier, vol. 17(1), pages 92-104, September.
    29. Jones, Brad & Lin, Chien-Ting & Masih, A. Mansur M., 2005. "Macroeconomic announcements, volatility, and interrelationships: An examination of the UK interest rate and equity markets," International Review of Financial Analysis, Elsevier, vol. 14(3), pages 356-375.
    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. Tomasz Schabek & Bojana Olgiæ Draženoviæ & Davor Mance, 2019. "Reaction of Zagreb Stock Exchange CROBEX Index to macroeconomic announcements within a high frequency time interval," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(2), pages 741-758.

    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. Henryk Gurgul & Lukasz Lach & Tomasz Wójtowicz, 2016. "Linear and nonlinear intraday causalities in response to U.S. macroeconomic news announcements: Evidence from Central Europe," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 17(2), pages 217-240.
    2. Koubaa, Yosra & Slim, Skander, 2019. "The relationship between trading activity and stock market volatility: Does the volume threshold matter?," Economic Modelling, Elsevier, vol. 82(C), pages 168-184.
    3. Carroll, Rachael & Kearney, Colm, 2015. "Testing the mixture of distributions hypothesis on target stocks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 1-14.
    4. Piotr Gurgul & Robert Syrek, 2013. "Testing of Dependencies between Stock Returns and Trading Volume by High Frequency Data," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 11(4 (Winter), pages 353-373.
    5. Do, Hung Xuan & Brooks, Robert & Treepongkaruna, Sirimon & Wu, Eliza, 2014. "How does trading volume affect financial return distributions?," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 190-206.
    6. Marina Balboa & Paulo M. M. Rodrigues & Antonio Rubia & A. M. Robert Taylor, 2021. "Multivariate fractional integration tests allowing for conditional heteroskedasticity with an application to return volatility and trading volume," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 544-565, August.
    7. Henryk Gurgul & Roland Mestel & Robert Syrek, 2008. "Polish Stock Market and some foreign markets - dependence analysis by copulas," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 18(2), pages 17-35.
    8. Pengfei Wang & Wei Zhang & Xiao Li & Dehua Shen, 2019. "Trading volume and return volatility of Bitcoin market: evidence for the sequential information arrival hypothesis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(2), pages 377-418, June.
    9. Chuang, Wen-I & Liu, Hsiang-Hsi & Susmel, Rauli, 2012. "The bivariate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility," Global Finance Journal, Elsevier, vol. 23(1), pages 1-15.
    10. Henryk Gurgul & Tomasz Wójtowicz, 2006. "Long-run properties of trading volume and volatility of equities listed in DJIA index," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 16(3-4), pages 29-56.
    11. Jawadi Fredj & Ureche-Rangau Loredana, 2013. "Threshold linkages between volatility and trading volume: evidence from developed and emerging markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 313-333, May.
    12. Mougoué, Mbodja & Aggarwal, Raj, 2011. "Trading volume and exchange rate volatility: Evidence for the sequential arrival of information hypothesis," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2690-2703, October.
    13. Chuang, Chia-Chang & Kuan, Chung-Ming & Lin, Hsin-Yi, 2009. "Causality in quantiles and dynamic stock return-volume relations," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1351-1360, July.
    14. Henryk Gurgul & Łukasz Lach, 2009. "Linear versus nonlinear causality for DAX companies," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 19(3), pages 27-46.
    15. Behrendt, Simon & Schmidt, Alexander, 2021. "Nonlinearity matters: The stock price – trading volume relation revisited," Economic Modelling, Elsevier, vol. 98(C), pages 371-385.
    16. Henryk Gurgul & Roland Mestel & Tomasz Wojtowicz, 2007. "Distribution of volume on the American stock market," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 1, pages 143-163.
    17. Kao, Yu-Sheng & Chuang, Hwei-Lin & Ku, Yu-Cheng, 2020. "The empirical linkages among market returns, return volatility, and trading volume: Evidence from the S&P 500 VIX Futures," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    18. Abhinava Tripathi, 2021. "The Arrival of Information and Price Adjustment Across Extreme Quantiles: Global Evidence," IIM Kozhikode Society & Management Review, , vol. 10(1), pages 7-19, January.
    19. Kausik Chaudhuri & Alok Kumar, 2015. "A Markov-Switching Model for Indian Stock Price and Volume," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 14(3), pages 239-257, December.
    20. David McMillan & Alan Speight, 2002. "Return-volume dynamics in UK futures," Applied Financial Economics, Taylor & Francis Journals, vol. 12(10), pages 707-713.

    More about this item

    Keywords

    Keywords: trading volume; return volatility; macroeconomic news; sequential information arrival; Granger causality;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:fau:fauart:v:66:y:2016:i:5:p:405-425. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/icunicz.html .

    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: Natalie Svarcova (email available below). General contact details of provider: https://edirc.repec.org/data/icunicz.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.