IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-18-00547.html
   My bibliography  Save this article

The systematic risk of gold at different time-scales

Author

Listed:
  • Antonis A Michis

    (Central Bank of Cyprus)

Abstract

Gold is frequently cited by investors as a financial asset that can be associated with a negative beta coefficient. I investigate this hypothesis by estimating the beta coefficient of gold at different time-scales and examining the associated implications for investors with different planning horizons. Estimation is performed using maximal overlap discrete wavelet transforms of gold and stock market returns in four major currencies. The results suggest that gold tends to be associated with a negative beta coefficient when considering long-term investment horizons, and this finding is consistent across markets and currencies.

Suggested Citation

  • Antonis A Michis, 2019. "The systematic risk of gold at different time-scales," Economics Bulletin, AccessEcon, vol. 39(2), pages 1215-1227.
  • Handle: RePEc:ebl:ecbull:eb-18-00547
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2019/Volume39/EB-19-V39-I2-P116.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    2. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    3. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    Full references (including those not matched with items on IDEAS)

    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. Amélie Charles & Olivier Darné & Jae H. Kim, 2014. "Precious metals shine? A market efficiency perspective," Working Papers hal-01010516, HAL.
    2. Bampinas, Georgios & Panagiotidis, Theodore, 2015. "Are gold and silver a hedge against inflation? A two century perspective," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 267-276.
    3. Berna Kirkulak Uludag & Zorikto Lkhamazhapov, 2014. "Long memory and structural breaks in the returns and volatility of gold: evidence from Turkey," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3777-3787, November.
    4. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2015. "Will precious metals shine? A market efficiency perspective," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 284-291.
    5. Lucey, Brian M. & Sharma, Susan Sunila & Vigne, Samuel A., 2017. "Gold and inflation(s) – A time-varying relationship," Economic Modelling, Elsevier, vol. 67(C), pages 88-101.
    6. Schweikert, Karsten, 2018. "Are gold and silver cointegrated? New evidence from quantile cointegrating regressions," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 44-51.
    7. Rehman, Mobeen Ur & Vinh Vo, Xuan, 2020. "Cryptocurrencies and precious metals: A closer look from diversification perspective," Resources Policy, Elsevier, vol. 66(C).
    8. Aye, Goodness & Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong, 2015. "Forecasting the price of gold using dynamic model averaging," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 257-266.
    9. Kearney, Fearghal & Shang, Han Lin & Sheenan, Lisa, 2019. "Implied volatility surface predictability: The case of commodity markets," Journal of Banking & Finance, Elsevier, vol. 108(C).
    10. Das, Debojyoti & Bhatia, Vaneet & Kumar, Surya Bhushan & Basu, Sankarshan, 2022. "Do precious metals hedge crude oil volatility jumps?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    11. Mongi Arfaoui & Aymen Ben Rejeb, 2017. "Oil, gold, US dollar and stock market interdependencies: a global analytical insight," European Journal of Management and Business Economics, Emerald Group Publishing Limited, vol. 26(3), pages 278-293, October.
    12. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Energies, MDPI, vol. 12(17), pages 1-17, September.
    13. Bakas, Dimitrios & Triantafyllou, Athanasios, 2020. "Commodity price volatility and the economic uncertainty of pandemics," Economics Letters, Elsevier, vol. 193(C).
    14. Tim Leung & Brian Ward, 2015. "The golden target: analyzing the tracking performance of leveraged gold ETFs," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(3), pages 278-297, August.
    15. Vicente J. Bolós & Rafael Benítez & Román Ferrer, 2020. "A New Wavelet Tool to Quantify Non-Periodicity of Non-Stationary Economic Time Series," Mathematics, MDPI, vol. 8(5), pages 1-16, May.
    16. Benjamin Hippert & André Uhde & Sascha Tobias Wengerek, 2019. "Portfolio benefits of adding corporate credit default swap indices: evidence from North America and Europe," Review of Derivatives Research, Springer, vol. 22(2), pages 203-259, July.
    17. Czudaj Robert L., 2020. "The role of uncertainty on agricultural futures markets momentum trading and volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-39, June.
    18. Tachibana, Minoru, 2022. "Safe haven assets for international stock markets: A regime-switching factor copula approach," Research in International Business and Finance, Elsevier, vol. 60(C).
    19. Goodell, John W. & Goutte, Stephane, 2021. "Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis," Finance Research Letters, Elsevier, vol. 38(C).
    20. Roman Mestre, 2021. "A wavelet approach of investing behaviors and their effects on risk exposures," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-37, December.

    More about this item

    Keywords

    systematic risk; time-scales; wavelets;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets

    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:ebl:ecbull:eb-18-00547. 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: John P. Conley (email available below). General contact details of provider: .

    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.