IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp1719.html
   My bibliography  Save this paper

Brexit and Uncertainty in Financial Markets

Author

Listed:
  • Guglielmo Maria Caporale
  • Luis A. Gil-Alana
  • Tommaso Trani

Abstract

This paper applies long-memory techniques (both parametric and semi-parametric) to examine whether Brexit has led to any significant changes in the degree of persistence of the FTSE 100 Implied Volatility Index (IVI) and of the British pound’s implied volatilities (IVs) vis-à-vis the main currencies traded in the FOREX, namely the euro, the US dollar and the Japanese yen. We split the sample to compare the stochastic properties of the series under investigation before and after the Brexit referendum, and find an increase in the degree of persistence in all cases except for the British pound-yen IV, whose persistence has declined after Brexit. These findings highlight the importance of completing swiftly the negotiations with the EU to achieve an appropriate Brexit deal.

Suggested Citation

  • Guglielmo Maria Caporale & Luis A. Gil-Alana & Tommaso Trani, 2018. "Brexit and Uncertainty in Financial Markets," Discussion Papers of DIW Berlin 1719, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1719
    as

    Download full text from publisher

    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.575924.de/dp1719.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jeff Fleming & Barbara Ostdiek & Robert E. Whaley, 1995. "Predicting stock market volatility: A new measure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(3), pages 265-302, May.
    2. Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2014. "Modeling and predicting the CBOE market volatility index," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 1-10.
    3. 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.
    4. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Is market fear persistent? A long-memory analysis," Finance Research Letters, Elsevier, vol. 27(C), pages 140-147.
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    6. Hui Guo & Robert F. Whitelaw, 2006. "Uncovering the Risk–Return Relation in the Stock Market," Journal of Finance, American Finance Association, vol. 61(3), pages 1433-1463, June.
    7. Luis A. Gil‐Alana, 2008. "Fractional integration and structural breaks at unknown periods of time," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(1), pages 163-185, January.
    8. Rafal Kierzenkowski & Nigel Pain & Elena Rusticelli & Sanne Zwart, 2016. "The Economic Consequences of Brexit: A Taxing Decision," OECD Economic Policy Papers 16, OECD Publishing.
    9. 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.
    10. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    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. Paul J. J. Welfens, 2019. "Lack of international risk management in BREXIT?," International Economics and Economic Policy, Springer, vol. 16(1), pages 103-160, March.
    2. Chen Gu & Ann Marie Hibbert, 2021. "Expectations and financial markets: Lessons from Brexit," The Financial Review, Eastern Finance Association, vol. 56(2), pages 279-299, May.
    3. Arthur Korus & Kaan Celebi, 2019. "The impact of Brexit news on British pound exchange rates," International Economics and Economic Policy, Springer, vol. 16(1), pages 161-192, March.
    4. Sarwar, Ghulam, 2020. "Interrelations in market fears of U.S. and European equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Andrikopoulos, Athanasios & Zheng, Min, 2023. "A dynamic analysis of the neglected firm effect," International Review of Financial Analysis, Elsevier, vol. 85(C).
    6. Samir Kadiric & Arthur Korus, 2019. "The effects of Brexit on credit spreads: Evidence from UK and Eurozone corporate bond markets," International Economics and Economic Policy, Springer, vol. 16(1), pages 65-102, March.
    7. Andrikopoulos, Athanasios & Dassiou, Xeni & Zheng, Min, 2020. "Exchange-rate exposure and Brexit: The case of FTSE, DAX and IBEX," International Review of Financial Analysis, Elsevier, vol. 68(C).
    8. Samir Kadiric & Arthur Korus, 2018. "Effects of Brexit on Corporate Yield Spreads: Evidence from UK and Eurozone Corporate Bond Markets," EIIW Discussion paper disbei251, Universitätsbibliothek Wuppertal, University Library.
    9. Tihana Škrinjarić, 2019. "Stock Market Reactions to Brexit: Case of Selected CEE and SEE Stock Markets," IJFS, MDPI, vol. 7(1), pages 1-14, January.
    10. Samir Kadiric, 2020. "The determinants of sovereign risk premiums in the UK and the European government bond market: The impact of Brexit," EIIW Discussion paper disbei271, Universitätsbibliothek Wuppertal, University Library.
    11. Samir Kadiric, 2022. "The determinants of sovereign risk premiums in the UK and the European government bond market: the impact of Brexit," International Economics and Economic Policy, Springer, vol. 19(2), pages 267-298, May.

    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. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Tripathy, Trilochan, 2020. "Volatility persistence in the Russian stock market," Finance Research Letters, Elsevier, vol. 32(C).
    2. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Is market fear persistent? A long-memory analysis," Finance Research Letters, Elsevier, vol. 27(C), pages 140-147.
    3. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    4. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.
    5. Guglielmo Maria Caporale & Luis A. Gil-Alana & Trilochan Tripathy, 2018. "Persistence in the Russian Stock Market Volatility Indices," CESifo Working Paper Series 7243, CESifo.
    6. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.
    7. Panagiotis Delis & Stavros Degiannakis & Konstantinos Giannopoulos, 2023. "What Should be Taken into Consideration when Forecasting Oil Implied Volatility Index?," The Energy Journal, , vol. 44(5), pages 231-250, September.
    8. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    9. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(1), pages 1-25, March.
    10. Campos, I. & Cortazar, G. & Reyes, T., 2017. "Modeling and predicting oil VIX: Internet search volume versus traditional mariables," Energy Economics, Elsevier, vol. 66(C), pages 194-204.
    11. Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2014. "Modeling and predicting the CBOE market volatility index," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 1-10.
    12. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    13. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    14. Elie Bouri & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Infectious Diseases, Market Uncertainty and Oil Market Volatility," Energies, MDPI, vol. 13(16), pages 1-8, August.
    15. Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
    16. Li, Xingyi & Zakamulin, Valeriy, 2020. "The term structure of volatility predictability," International Journal of Forecasting, Elsevier, vol. 36(2), pages 723-737.
    17. Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui, 2023. "Stock market volatility predictability in a data-rich world: A new insight," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1804-1819.
    18. Li, Tao & Ma, Feng & Zhang, Xuehua & Zhang, Yaojie, 2020. "Economic policy uncertainty and the Chinese stock market volatility: Novel evidence," Economic Modelling, Elsevier, vol. 87(C), pages 24-33.
    19. Degiannakis, Stavros, 2018. "Multiple days ahead realized volatility forecasting: Single, combined and average forecasts," Global Finance Journal, Elsevier, vol. 36(C), pages 41-61.
    20. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.

    More about this item

    Keywords

    Brexit; uncertainty; IVI index; British pound’s implied volatilities; financial markets;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F30 - International Economics - - International Finance - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:diw:diwwpp:dp1719. 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: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/diwbede.html .

    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.