IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2020-02-22.html
   My bibliography  Save this article

The Effect of Energy Cryptos on Efficient Portfolios of Key Energy Listed Companies in the S&P Composite 1500 Energy Index

Author

Listed:
  • Ikhlaas Gurrib

    (Canadian University Dubai, School of Graduate Studies, Sheikh Zayed Road, Dubai, UAE,)

  • Elgilani Elsharief

    (Canadian University Dubai, School of Graduate Studies, Sheikh Zayed Road, Dubai, UAE,)

  • Firuz Kamalov

    (Canadian University Dubai, Faculty of Engineering and Architecture, Sheikh Zayed Road, Dubai, UAE.)

Abstract

This paper investigates if energy block chain based crypto currencies can help diversify equity portfolios consisting primarily of leading energy companies of the US S&P Composite 1500 energy index. Key contributions are in terms of assessing the importance of energy cryptos as alternative investments in portfolio management, and whether different volatility models such as autoregressive moving average Generalized Autoregressive Heteroskedasticity (ARMA-GARCH) and machine learning (ML) can help investors make better investment decisions. The methodology utilizes the traditional Markowitz mean-variance framework to obtain optimized portfolio combinations. Volatility measures, derived from the Cornish-Fisher adjusted variance, ARMA family classes and ML models are used to compare efficient portfolios. The study also analyses the effect of adding cryptos to equity portfolios with non-positive excess returns. Different models are assessed using the Sharpe performance measure. Daily data is used, spanning from November 21, 2017 to January 31, 2019. Findings suggest that energy based cryptos do not have a significant impact on energy equity portfolios, despite the use of different risk measures. This is attributable to the relatively poor performance of energy cryptos which did not contribute in improving the excess return per unit of risk of efficient portfolios based on the leading US energy stocks.

Suggested Citation

  • Ikhlaas Gurrib & Elgilani Elsharief & Firuz Kamalov, 2020. "The Effect of Energy Cryptos on Efficient Portfolios of Key Energy Listed Companies in the S&P Composite 1500 Energy Index," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 179-193.
    • Handle: RePEc:eco:journ2:2020-02-22
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/8676/4880
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijeep/article/view/8676/4880
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2009. "Jackknife Estimator for Tracking Error Variance of Optimal Portfolios," Management Science, INFORMS, vol. 55(6), pages 990-1002, June.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Michael D. Bordo, 1981. "The classical gold standard: some lessons for today," Review, Federal Reserve Bank of St. Louis, vol. 63(May), pages 2-17.
    4. Abdelhakim Aknouche & Abdelouahab Bibi, 2009. "Quasi‐maximum likelihood estimation of periodic GARCH and periodic ARMA‐GARCH processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 19-46, January.
    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. Naomi Pandiangan & Sukono Sukono & Endang Soeryana Hasbullah, 2021. "Quadratic Investment Portfolio Based on Value-at-risk with Risk-Free Assets: For Stocks of the Mining and Energy Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 175-184.

    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. Aknouche, Abdelhakim & Demmouche, Nacer & Touche, Nassim, 2018. "Bayesian MCMC analysis of periodic asymmetric power GARCH models," MPRA Paper 91136, University Library of Munich, Germany.
    2. Regnard, Nazim & Zakoïan, Jean-Michel, 2011. "A conditionally heteroskedastic model with time-varying coefficients for daily gas spot prices," Energy Economics, Elsevier, vol. 33(6), pages 1240-1251.
    3. Aknouche, Abdelhakim & Al-Eid, Eid & Demouche, Nacer, 2016. "Generalized quasi-maximum likelihood inference for periodic conditionally heteroskedastic models," MPRA Paper 75770, University Library of Munich, Germany, revised 19 Dec 2016.
    4. 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).
    5. 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).
    6. Cho, Guedae & Kim, MinKyoung & Koo, Won W., 2003. "Relative Agricultural Price Changes In Different Time Horizons," 2003 Annual meeting, July 27-30, Montreal, Canada 22249, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    8. Umar, Muhammad & Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Furqan, Mehreen, 2023. "Asymmetric volatility structure of equity returns: Evidence from an emerging market," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 330-336.
    9. Shively, Gerald E., 2001. "Price thresholds, price volatility, and the private costs of investment in a developing country grain market," Economic Modelling, Elsevier, vol. 18(3), pages 399-414, August.
    10. Lahmiri, Salim & Bekiros, Stelios, 2017. "Disturbances and complexity in volatility time series," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 38-42.
    11. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    12. 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.
    13. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    14. Jumah, Adusei & Kunst, Robert M., 2001. "The Effects of Exchange-Rate Exposures on Equity Asset Markets," Economics Series 94, Institute for Advanced Studies.
    15. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    16. Gruener Hans Peter & Hayo Bernd & Hefeker Carsten, 2009. "Unions, Wage Setting and Monetary Policy Uncertainty," The B.E. Journal of Macroeconomics, De Gruyter, vol. 9(1), pages 1-25, October.
    17. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    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. Taoufik Bouezmarni & Mohamed Doukali & Abderrahim Taamouti, 2023. "Testing Granger Non-Causality in Expectiles," University of East Anglia School of Economics Working Paper Series 2023-02, School of Economics, University of East Anglia, Norwich, UK..
    20. ?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.

    More about this item

    Keywords

    Equity Portfolios; Energy Cryptos; Performance Evaluation; Machine Learning; Volatility Measure;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:eco:journ2:2020-02-22. 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: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.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.