IDEAS home Printed from https://ideas.repec.org/a/ibn/ijefaa/v15y2023i1p24.html
   My bibliography  Save this article

How Volatility and Herding of the Stock Markets in the Oceania Region Influence Investors and Policymakers: A Sector-Wise Exploration in Pre and Post-COVID Period

Author

Listed:
  • Swarnil Roy
  • Sk. Riad Arefin
  • Avijit Mallik

Abstract

The paper probes the sector-wise presence of volatility persistence, herding behavior and corresponding implications on investors and policymakers in the Oceania region both in Pre-COVID & Post-COVID era. The inspection is based on seven identical sectors from both Australia and New Zealand using GARCH (Generalized autoregressive conditional heteroscedasticity) methods for volatility analysis and CSAD (Cross-Sectional Absolute Deviation) method for herding behavior. This paper finds the existence of herding behavior only in the consumer discretionary sector for both countries which delineates efficient market conditions for other sectors. The market is highly favorable for the investors in Food & Beverages, IT, and Healthcare sectors in both countries due to the potential growth opportunity while Real Estate and Financial sectors should be meticulously assessed in line with the alteration of macroeconomic forces. Fiscal and monetary measures along with the influx of labor forces and technological breakthroughs should be the key concentrations for the policymakers of both countries.

Suggested Citation

  • Swarnil Roy & Sk. Riad Arefin & Avijit Mallik, 2023. "How Volatility and Herding of the Stock Markets in the Oceania Region Influence Investors and Policymakers: A Sector-Wise Exploration in Pre and Post-COVID Period," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 15(1), pages 1-24, January.
    • Handle: RePEc:ibn:ijefaa:v:15:y:2023:i:1:p:24
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/ijef/article/download/0/0/48153/51759
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/ijef/article/view/0/48153
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Markus Brueckner & Joaquin Vespignani, 2021. "COVID‐19 Infections and the Performance of the Stock Market: An Empirical Analysis for Australia," Economic Papers, The Economic Society of Australia, vol. 40(3), pages 173-193, September.
    2. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Estimating yield spreads volatility using GARCH-type models," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    3. Chen, Rongda & Bao, Weiwei & Jin, Chenglu, 2021. "Investor sentiment and predictability for volatility on energy futures Markets: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 112-129.
    4. M. Humayun Kabir, 2018. "Did Investors Herd during the Financial Crisis? Evidence from the US Financial Industry," International Review of Finance, International Review of Finance Ltd., vol. 18(1), pages 59-90, March.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Scarcioffolo, Alexandre R. & Etienne, Xiaoli L., 2021. "Regime-switching energy price volatility: The role of economic policy uncertainty," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 336-356.
    7. Uddin, Moshfique & Chowdhury, Anup & Anderson, Keith & Chaudhuri, Kausik, 2021. "The effect of COVID – 19 pandemic on global stock market volatility: Can economic strength help to manage the uncertainty?," Journal of Business Research, Elsevier, vol. 128(C), pages 31-44.
    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. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(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. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    2. Ștefan Cristian Gherghina & Daniel Ștefan Armeanu & Camelia Cătălina Joldeș, 2021. "COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach," JRFM, MDPI, vol. 14(8), pages 1-29, July.
    3. Samet Gunay & Walid Bakry & Somar Al-Mohamad, 2021. "The Australian Stock Market’s Reaction to the First Wave of the COVID-19 Pandemic and Black Summer Bushfires: A Sectoral Analysis," JRFM, MDPI, vol. 14(4), pages 1-19, April.
    4. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    5. Kamal, Javed Bin & Wohar, Mark, 2023. "Heterogenous responses of stock markets to covid related news and sentiments: Evidence from the 1st year of pandemic," International Economics, Elsevier, vol. 173(C), pages 68-85.
    6. Katarzyna Kuziak & Joanna Górka, 2023. "Dependence Analysis for the Energy Sector Based on Energy ETFs," Energies, MDPI, vol. 16(3), pages 1-30, January.
    7. Budi Setiawan & Marwa Ben Abdallah & Maria Fekete-Farkas & Robert Jeyakumar Nathan & Zoltan Zeman, 2021. "GARCH (1,1) Models and Analysis of Stock Market Turmoil during COVID-19 Outbreak in an Emerging and Developed Economy," JRFM, MDPI, vol. 14(12), pages 1-19, December.
    8. Bao Cong Nguyen To & Tam Van Thien Nguyen & Nham Thi Hong Nguyen & Hoai Thu Ho, 2022. "Responses of the International Bond Markets to COVID-19 Containment Measures," JRFM, MDPI, vol. 15(3), pages 1-11, March.
    9. Mhd Ruslan, Siti Marsila & Mokhtar, Kasypi, 2021. "Stock market volatility on shipping stock prices: GARCH models approach," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).
    10. Chen, Wang & Lu, Xinjie & Wang, Jiqian, 2022. "Modeling and managing stock market volatility using MRS-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 625-635.
    11. Seiler, Volker, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 160-179.
    12. Beran, Jan & Feng, Yuanhua, 1999. "Local Polynomial Estimation with a FARIMA-GARCH Error Process," CoFE Discussion Papers 99/08, University of Konstanz, Center of Finance and Econometrics (CoFE).
    13. Corbet, Shaen & Larkin, Charles & McMullan, Caroline, 2020. "The impact of industrial incidents on stock market volatility," Research in International Business and Finance, Elsevier, vol. 52(C).
    14. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    15. Lahmiri, Salim & Bekiros, Stelios, 2017. "Disturbances and complexity in volatility time series," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 38-42.
    16. Tomanova, Lucie, 2013. "Exchange Rate Volatility and the Foreign Trade in CEEC," EY International Congress on Economics I (EYC2013), October 24-25, 2013, Ankara, Turkey 267, Ekonomik Yaklasim Association.
    17. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    18. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    19. ?ikolaos A. Kyriazis, 2021. "Impacts of Stock Indices, Oil, and Twitter Sentiment on Major Cryptocurrencies during the COVID-19 First Wave," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 133-146.
    20. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:ibn:ijefaa:v:15:y:2023:i:1:p:24. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.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.