IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v34y2020ics1544612319301837.html
   My bibliography  Save this article

Impact of Brexit vote on the London stock exchange: A sectorial analysis of its volatility and efficiency

Author

Listed:
  • Arshad, Shaista
  • Rizvi, Syed Aun R.
  • Haroon, Omair

Abstract

The introduction and continual ambiguity surrounding Brexit vote has brought attention to the volatility of the London Stock Exchange (LSE). Using wavelets to decompose financial time series into short-term and long-term components, we study the volatility and efficiency of the different sectors in LSE around Brexit vote events. The results reveal that while the overall volatility reduced, various sectors within LSE behaved differently. Furthermore, the efficiency of the British stock market worsened drastically during the uncertainty of the Brexit vote.

Suggested Citation

  • Arshad, Shaista & Rizvi, Syed Aun R. & Haroon, Omair, 2020. "Impact of Brexit vote on the London stock exchange: A sectorial analysis of its volatility and efficiency," Finance Research Letters, Elsevier, vol. 34(C).
    • Handle: RePEc:eee:finlet:v:34:y:2020:i:c:s1544612319301837
    DOI: 10.1016/j.frl.2019.07.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612319301837
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2019.07.013?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. Bacry, E. & Delour, J. & Muzy, J.F., 2001. "Modelling financial time series using multifractal random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 84-92.
    2. Lee A. Smales, 2017. "“Brexit”: A Case Study in the Relationship Between Political and Financial Market Uncertainty," International Review of Finance, International Review of Finance Ltd., vol. 17(3), pages 451-459, September.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2016. "Intraday volatility interaction between the crude oil and equity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 1-13.
    5. Dewandaru, Ginanjar & Rizvi, Syed Aun R. & Masih, Rumi & Masih, Mansur & Alhabshi, Syed Othman, 2014. "Stock market co-movements: Islamic versus conventional equity indices with multi-timescales analysis," Economic Systems, Elsevier, vol. 38(4), pages 553-571.
    6. Joakim Westerlund & Paresh Narayan, 2015. "Testing for Predictability in Conditionally Heteroskedastic Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 342-375.
    7. Rizvi, Syed Aun R. & Arshad, Shaista, 2017. "Analysis of the efficiency–integration nexus of Japanese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 296-308.
    8. Schiereck, D. & Kiesel, F. & Kolaric, S., 2016. "Brexit: (Not) another Lehman moment for banks?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 82881, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Jamal Bouoiyour, Refk Selmi, 2018. "Are UK industries resilient in dealing with uncertainty? The case of Brexit," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 15(2), pages 277-292, December.
    10. Caetano, Marco Antonio Leonel & Yoneyama, Takashi, 2012. "A method for detection of abrupt changes in the financial market combining wavelet decomposition and correlation graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4877-4882.
    11. Arshad, Shaista & Rizvi, Syed Aun R., 2015. "The troika of business cycle, efficiency and volatility. An East Asian perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 158-170.
    12. repec:idn:journl:v:1:y:2019:i:sp4:p:1-16 is not listed on IDEAS
    13. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    14. Rizvi, Syed Aun R. & Arshad, Shaista & Alam, Nafis, 2018. "A tripartite inquiry into volatility-efficiency-integration nexus - case of emerging markets," Emerging Markets Review, Elsevier, vol. 34(C), pages 143-161.
    15. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2015. "Are Indian stock returns predictable?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 506-531.
    16. Deepa & Paresh K Narayan, "undated". "Are Indian Stock Returns Predictable?," Working Papers 2015_07, Deakin University, Department of Economics.
    17. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
    18. Adesina, Tola, 2017. "Estimating volatility persistence under a Brexit-vote structural break," Finance Research Letters, Elsevier, vol. 23(C), pages 65-68.
    19. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    20. Schiereck, Dirk & Kiesel, Florian & Kolaric, Sascha, 2016. "Brexit: (Not) another Lehman moment for banks?," Finance Research Letters, Elsevier, vol. 19(C), pages 291-297.
    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. Guidolin, Massimo & Pedio, Manuela, 2021. "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," Finance Research Letters, Elsevier, vol. 42(C).
    2. Kuzu, Erkan & Süsay, Aynur & Tanrıöven, Cihan, 2022. "A model study for calculation of the temperatures of major stock markets in the world with the quantum simulation and determination of the crisis periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    3. Deniz Erer & Elif Erer & Selim Güngör, 2023. "The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    4. Alexander Koch & Toan Luu Duc Huynh & Mei Wang, 2024. "News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 5-34, January.
    5. Abuzayed, Bana & Al-Fayoumi, Nedal & Bouri, Elie, 2022. "Hedging UK stock portfolios with gold and oil: The impact of Brexit," Resources Policy, Elsevier, vol. 75(C).

    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. Rizvi, Syed Aun R. & Arshad, Shaista, 2018. "Understanding time-varying systematic risks in Islamic and conventional sectoral indices," Economic Modelling, Elsevier, vol. 70(C), pages 561-570.
    2. 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.
    3. Rizvi, Syed Aun R. & Arshad, Shaista, 2017. "Analysis of the efficiency–integration nexus of Japanese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 296-308.
    4. Haroon, Omair & Rizvi, Syed Aun R., 2020. "COVID-19: Media coverage and financial markets behavior—A sectoral inquiry," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    5. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    6. repec:idn:journl:v:1:y:2019:i:sp2:p:1-12 is not listed on IDEAS
    7. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Tran, Vuong Thao, 2018. "Can economic policy uncertainty predict stock returns? Global evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 134-150.
    8. Arshad, Shaista & Rizvi, Syed Aun R. & Haroon, Omair & Mehmood, Fahad & Gong, Qiang, 2021. "Are oil prices efficient?," Economic Modelling, Elsevier, vol. 96(C), pages 362-370.
    9. Rizvi, Syed Aun R. & Arshad, Shaista & Alam, Nafis, 2018. "A tripartite inquiry into volatility-efficiency-integration nexus - case of emerging markets," Emerging Markets Review, Elsevier, vol. 34(C), pages 143-161.
    10. Narayan, Paresh Kumar & Sharma, Susan Sunila & Phan, Dinh Hoang Bach, 2016. "Asset price bubbles and economic welfare," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 139-148.
    11. Afees A. Salisu & Ahamuefula E. Ogbonna & Idris Adediran, 2021. "Stock‐induced Google trends and the predictability of sectoral stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 327-345, March.
    12. Salisu, Afees A. & Swaray, Raymond & Oloko, Tirimisiyu F., 2019. "Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables," Economic Modelling, Elsevier, vol. 76(C), pages 153-171.
    13. Narayan, Paresh Kumar & Liu, Ruipeng & Westerlund, Joakim, 2016. "A GARCH model for testing market efficiency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 121-138.
    14. Cikiryel, Burak & Masih, Mansur, 2017. "The Impact of Brexit on Islamic Stock Markets Employing MGARCH-DCC and Wavelet Correlation Analysis," MPRA Paper 95681, University Library of Munich, Germany.
    15. Alqahtani, Abdullah & Bouri, Elie & Vo, Xuan Vinh, 2020. "Predictability of GCC stock returns: The role of geopolitical risk and crude oil returns," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 239-249.
    16. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
    17. David G. McMillan, 2017. "Stock return predictability: the role of inflation and threshold dynamics," International Review of Applied Economics, Taylor & Francis Journals, vol. 31(3), pages 357-375, May.
    18. Afees A. Salisu & Juncal Cunado & Kazeem Isah & Rangan Gupta, 2020. "Oil Price and Exchange Rate Behaviour of the BRICS for Over a Century," Working Papers 202064, University of Pretoria, Department of Economics.
    19. Vu Ngoc Nguyen & Dat Thanh Nguyen, 2020. "Can Crude Oil Price be a Predictor of Stock Index Return? Evidence from Vietnamese Stock Market," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(1), pages 13-21, January.
    20. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2020. "Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis," JRFM, MDPI, vol. 13(10), pages 1-18, October.
    21. Kuo, Chen-Yin, 2016. "Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory," Economic Modelling, Elsevier, vol. 52(PB), pages 772-789.

    More about this item

    Keywords

    Brexit; Volatility; Sectoral; Equity market; Efficiency; Multifractal;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    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:finlet:v:34:y:2020:i:c:s1544612319301837. 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/frl .

    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.