IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v11y2023i4p130-d1273741.html
   My bibliography  Save this article

Extreme Value Theory Modelling of the Behaviour of Johannesburg Stock Exchange Financial Market Data

Author

Listed:
  • Maashele Kholofelo Metwane

    (Department of Statistics and Operations Research, University of Limpopo, Private Bag X1106, Sovenga 0727, South Africa)

  • Daniel Maposa

    (Department of Statistics and Operations Research, University of Limpopo, Private Bag X1106, Sovenga 0727, South Africa)

Abstract

Financial market data are abundant with outliers, and the search for an appropriate extreme value theory (EVT) approach to apply is an endless debate in the statistics of extremes research. This paper uses EVT methods to model the five-year daily all-share total return index (ALSTRI) and the daily United States dollar (USD) against the South African rand (ZAR) exchange rate of the Johannesburg stock exchange (JSE). The study compares the block maxima approach and the peaks-over-threshold (POT) approach in terms of their ability to model financial market data. The 100-year return levels for the block maxima approach were found to be almost equal to the maximum observations of the financial markets of 10,860 and R18.99 for the ALSTRI and the USD–ZAR, respectively. For the peaks-over-threshold (POT) approach, the results show that the ALSTRI and the USD–ZAR exchange rate will surpass 17,501.63 and R23.72, respectively, at least once in 100 years. The findings in this study reveal a clear distinction between block maxima and POT return level estimates. The POT approach return level estimates were comparably higher than the block maxima estimates. The study further revealed that the blended generalised extreme value (bGEVD) is more suitable for relatively short-term forecasting, since it cuts off at the 50-year return level. Therefore, this study will add value to the literature and knowledge of statistics and econometrics. In the future, more studies on bGEVD, vine copulas, and the r -largest-order bGEVD can be conducted in the financial markets.

Suggested Citation

  • Maashele Kholofelo Metwane & Daniel Maposa, 2023. "Extreme Value Theory Modelling of the Behaviour of Johannesburg Stock Exchange Financial Market Data," IJFS, MDPI, vol. 11(4), pages 1-27, November.
    • Handle: RePEc:gam:jijfss:v:11:y:2023:i:4:p:130-:d:1273741
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/11/4/130/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/11/4/130/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Silius M. Vandeskog & Sara Martino & Daniela Castro-Camilo & Håvard Rue, 2022. "Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 598-621, December.
    2. Karmakar, Madhusudan & Shukla, Girja K., 2015. "Managing extreme risk in some major stock markets: An extreme value approach," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 1-25.
    3. Emmanuel Afuecheta & Chigozie Utazi & Edmore Ranganai & Chibuzor Nnanatu, 2023. "An Application of Extreme Value Theory for Measuring Financial Risk in BRICS Economies," Annals of Data Science, Springer, vol. 10(2), pages 251-290, April.
    4. Lingling Qian & Yuexiang Jiang & Huaigang Long, 2023. "Extreme risk spillovers between China and major international stock markets," Modern Finance, Modern Finance Institute, vol. 1(1), pages 30-34.
    5. Wei, Xiaoyun & Han, Liyan, 2021. "The impact of COVID-19 pandemic on transmission of monetary policy to financial markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    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. A. H Nzokem, 2024. "Fitting the seven-parameter Generalized Tempered Stable distribution to the financial data," Papers 2410.19751, arXiv.org.

    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. Juncal Cunado & David Gabauer & Rangan Gupta, 2024. "Realized volatility spillovers between energy and metal markets: a time-varying connectedness approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-17, December.
    2. Fang, Sheng & Egan, Paul, 2018. "Measuring contagion effects between crude oil and Chinese stock market sectors," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 31-38.
    3. Meng, Fanting, 2024. "Driving sustainable development: Fiscal policy and the promotion of natural resource efficiency," Resources Policy, Elsevier, vol. 90(C).
    4. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    5. Wang, Xinya & Lucey, Brian & Huang, Shupei, 2022. "Can gold hedge against oil price movements: Evidence from GARCH-EVT wavelet modeling," Journal of Commodity Markets, Elsevier, vol. 27(C).
    6. Emmanuel Joel Aikins Abakah & Guglielmo Maria Caporale & Luis A. Gil-Alana, 2021. "The Impact of Containment Measures and Monetary and Fiscal Responses on US Financial Markets during the Covid-19 Pandemic," CESifo Working Paper Series 9163, CESifo.
    7. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.
    8. Sahamkhadam, Maziar & Stephan, Andreas & Östermark, Ralf, 2018. "Portfolio optimization based on GARCH-EVT-Copula forecasting models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 497-506.
    9. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
    10. Shan, Yimin & Chen, Yang & Xiao, Yajun, 2023. "Monetary policy as market stabilizer in the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 55(PB).
    11. Prabheesh, K.P. & Sasongko, Aryo & Indawan, Fiskara, 2023. "Did the policy responses influence credit and business cycle co-movement during the COVID-19 crisis? Evidence from Indonesia," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 243-255.
    12. Carlos J. Rincon & Anastasiia V. Petrova, 2024. "Assessing the Impact of the ECB’s Unconventional Monetary Policy on the European Stock Markets," JRFM, MDPI, vol. 17(9), pages 1-20, September.
    13. Humphred Watard & Saidou Baba Oumar & Nkiendem Felix, 2024. "Volatility spillover effect analysis of African emerging stock exchange markets: 2018–2023," SN Business & Economics, Springer, vol. 4(10), pages 1-30, October.
    14. Yang, Jianlei, 2024. "Financial stability policy and downside risk in stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    15. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Emmanuel Joel Aikins Abakah, 2023. "US policy responses to the COVID-19 pandemic and sectoral stock indices: A fractional integration approach," Applied Economics, Taylor & Francis Journals, vol. 55(3), pages 283-292, January.
    16. Imalka Wasana Rathnayaka & Rasheda Khanam & Mohammad Mafizur Rahman, 2024. "Examining Monetary Policy Measures and Their Impacts during and after the COVID Era: OECD Perspectives," Economies, MDPI, vol. 12(6), pages 1-17, June.
    17. Samit Paul & Madhusudan Karmakar, 2017. "Relative Efficiency of Component GARCH-EVT Approach in Managing Intraday Market Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 21(4), pages 247-283, December.
    18. Emre Cevik & Buket Kirci Altinkeski & Emrah Ismail Cevik & Sel Dibooglu, 2022. "Investor sentiments and stock markets during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-34, December.
    19. Choi, Sun-Yong, 2022. "Credit risk interdependence in global financial markets: Evidence from three regions using multiple and partial wavelet approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    20. Bȩdowska-Sójka, Barbara & Kliber, Agata, 2022. "Impact of COVID-19 on sovereign risk: Latin America versus Asia," Finance Research Letters, Elsevier, vol. 47(PA).

    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:gam:jijfss:v:11:y:2023:i:4:p:130-:d:1273741. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.