IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03512893.html
   My bibliography  Save this paper

“Shiny” crypto assets: A systemic look at gold-backed cryptocurrencies during the COVID-19 pandemic

Author

Listed:
  • Akanksha Jalan

    (ESC [Rennes] - ESC Rennes School of Business)

  • Roman Matkovskyy

    (ESC [Rennes] - ESC Rennes School of Business)

  • Larisa Yarovaya

    (SBS - Southampton Business School)

Abstract

In this paper, we empirically analyse the performance of five gold-backed stablecoins during the COVID-19 pandemic and compare them to gold, Bitcoin and Tether. In the digital assets' ecosystem, gold-backed cryptocurrencies have the potential to address regulatory and policy concerns by decreasing volatility of cryptocurrency prices and facilitating broader cryptocurrency adoption. We find that during the COVID-19 pandemic, gold-backed cryptocurrencies were susceptible to volatility transmitted from gold markets. Our results indicate that for the selected gold-backed cryptocurrencies, their volatility, and as a consequence, risks associated with volatility, remained comparable to the Bitcoin. In addition, gold-backed cryptocurrencies did not show safe-haven potential comparable to their underlying precious metal, gold.

Suggested Citation

  • Akanksha Jalan & Roman Matkovskyy & Larisa Yarovaya, 2021. "“Shiny” crypto assets: A systemic look at gold-backed cryptocurrencies during the COVID-19 pandemic," Post-Print hal-03512893, HAL.
  • Handle: RePEc:hal:journl:hal-03512893
    DOI: 10.1016/j.irfa.2021.101958
    Note: View the original document on HAL open archive server: https://rennes-sb.hal.science/hal-03512893
    as

    Download full text from publisher

    File URL: https://rennes-sb.hal.science/hal-03512893/document
    Download Restriction: no

    File URL: https://libkey.io/10.1016/j.irfa.2021.101958?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lee, Tae-Hwy & Yang, Weiping, 2014. "Granger-causality in quantiles between financial markets: Using copula approach," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 70-78.
    2. Matkovskyy, Roman & Jalan, Akanksha & Dowling, Michael, 2020. "Effects of economic policy uncertainty shocks on the interdependence between Bitcoin and traditional financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 150-155.
    3. Rafael Schmidt & Ulrich Stadtmüller, 2006. "Non‐parametric Estimation of Tail Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 307-335, June.
    4. Maghyereh, Aktham & Abdoh, Hussein, 2020. "Tail dependence between Bitcoin and financial assets: Evidence from a quantile cross-spectral approach," International Review of Financial Analysis, Elsevier, vol. 71(C).
    5. Yang, Yang & Zhao, Zhao, 2020. "Quantile nonlinear unit root test with covariates and an application to the PPP hypothesis," Economic Modelling, Elsevier, vol. 93(C), pages 728-736.
    6. Nguyen, Cuong C. & Bhatti, M. Ishaq, 2012. "Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 758-773.
    7. Kristoufek, Ladislav, 2021. "Tethered, or Untethered? On the interplay between stablecoins and major cryptoassets," Finance Research Letters, Elsevier, vol. 43(C).
    8. Geert Bekaert & Campbell R. Harvey & Christian Lundblad, 2007. "Liquidity and Expected Returns: Lessons from Emerging Markets," The Review of Financial Studies, Society for Financial Studies, vol. 20(6), pages 1783-1831, November.
    9. Acharya, Viral V. & Pedersen, Lasse Heje, 2005. "Asset pricing with liquidity risk," Journal of Financial Economics, Elsevier, vol. 77(2), pages 375-410, August.
    10. Galvao Jr., Antonio F., 2009. "Unit root quantile autoregression testing using covariates," Journal of Econometrics, Elsevier, vol. 152(2), pages 165-178, October.
    11. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    12. John M. Griffin & Amin Shams, 2020. "Is Bitcoin Really Untethered?," Journal of Finance, American Finance Association, vol. 75(4), pages 1913-1964, August.
    13. Tam Cho, Wendy K. & Gaines, Brian J., 2007. "Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance," The American Statistician, American Statistical Association, vol. 61, pages 218-223, August.
    14. Bekaert, Geert & Harvey, Campbell R., 1997. "Emerging equity market volatility," Journal of Financial Economics, Elsevier, vol. 43(1), pages 29-77, January.
    15. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    16. Frahm, Gabriel & Junker, Markus & Schmidt, Rafael, 2005. "Estimating the tail-dependence coefficient: Properties and pitfalls," Insurance: Mathematics and Economics, Elsevier, vol. 37(1), pages 80-100, August.
    17. Akanksha Jalan & Roman Matkovskyy & Andrew Urquhart, 2021. "What effect did the introduction of Bitcoin futures have on the Bitcoin spot market?," The European Journal of Finance, Taylor & Francis Journals, vol. 27(13), pages 1251-1281, September.
    18. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    19. Roman Matkovskyy, 2020. "A measurement of affluence and poverty interdependence across countries: Evidence from the application of tail copula," Bulletin of Economic Research, Wiley Blackwell, vol. 72(4), pages 404-416, October.
    20. Forbes, William P, 1996. "Picking Winners? A Survey of the Mean Reversion and Overreaction of Stock Prices Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 10(2), pages 123-158, June.
    21. Druică, Elena & Oancea, Bogdan & Vâlsan, Călin, 2018. "Benford's law and the limits of digit analysis," International Journal of Accounting Information Systems, Elsevier, vol. 31(C), pages 75-82.
    22. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
    23. Klein, Tony & Pham Thu, Hien & Walther, Thomas, 2018. "Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 105-116.
    24. Gandal, Neil & Hamrick, JT & Moore, Tyler & Oberman, Tali, 2018. "Price manipulation in the Bitcoin ecosystem," Journal of Monetary Economics, Elsevier, vol. 95(C), pages 86-96.
    25. Pennec, Guénolé Le & Fiedler, Ingo & Ante, Lennart, 2021. "Wash trading at cryptocurrency exchanges," Finance Research Letters, Elsevier, vol. 43(C).
    26. Yarovaya, Larisa & Matkovskyy, Roman & Jalan, Akanksha, 2022. "The COVID-19 black swan crisis: Reaction and recovery of various financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    27. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    28. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    29. Andreas Diekmann, 2007. "Not the First Digit! Using Benford's Law to Detect Fraudulent Scientif ic Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 321-329.
    30. Lee, Kuan-Hui, 2011. "The world price of liquidity risk," Journal of Financial Economics, Elsevier, vol. 99(1), pages 136-161, January.
    31. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    32. Chung, Kee H. & Zhang, Hao, 2014. "A simple approximation of intraday spreads using daily data," Journal of Financial Markets, Elsevier, vol. 17(C), pages 94-120.
    33. Conlon, Thomas & McGee, Richard, 2020. "Safe haven or risky hazard? Bitcoin during the Covid-19 bear market," Finance Research Letters, Elsevier, vol. 35(C).
    34. Wang, Gang-Jin & Ma, Xin-yu & Wu, Hao-yu, 2020. "Are stablecoins truly diversifiers, hedges, or safe havens against traditional cryptocurrencies as their name suggests?," Research in International Business and Finance, Elsevier, vol. 54(C).
    35. Roger Koenker & Zhijie Xiao, 2004. "Unit Root Quantile Autoregression Inference," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 775-787, January.
    36. Giacomini, Enzo & Härdle, Wolfgang & Spokoiny, Vladimir, 2009. "Inhomogeneous Dependence Modeling with Time-Varying Copulae," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 224-234.
    37. Easley, David & O'Hara, Maureen & Basu, Soumya, 2019. "From mining to markets: The evolution of bitcoin transaction fees," Journal of Financial Economics, Elsevier, vol. 134(1), pages 91-109.
    38. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    39. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    40. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2008. "Liquidity and market efficiency," Journal of Financial Economics, Elsevier, vol. 87(2), pages 249-268, February.
    41. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    42. Asimit, Alexandru V. & Gerrard, Russell & Hou, Yanxi & Peng, Liang, 2016. "Tail dependence measure for examining financial extreme co-movements," Journal of Econometrics, Elsevier, vol. 194(2), pages 330-348.
    43. Senyo, PK & Osabutey, Ellis L.C., 2020. "Unearthing antecedents to financial inclusion through FinTech innovations," Technovation, Elsevier, vol. 98(C).
    44. Yannick Malevergne & Didier Sornette, 2004. "How to account for extreme co-movements between individual stocks and the market," Post-Print hal-02312885, HAL.
    45. Linda M. Schilling & Harald Uhlig, 2019. "Currency Substitution under Transaction Costs," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 83-87, May.
    46. Ling Hu, 2006. "Dependence patterns across financial markets: a mixed copula approach," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 717-729.
    47. Roman Matkovskyy, 2019. "Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 667-698, September.
    48. Yang, Lu & Hamori, Shigeyuki, 2014. "Dependence structure between CEEC-3 and German government securities markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 29(C), pages 109-125.
    49. Jondeau, Eric, 2016. "Asymmetry in tail dependence in equity portfolios," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 351-368.
    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. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    2. Sruthy Madhavan & S. Sreejith, 2022. "A Comparative Analysis on the Role and Market Linkages of Gold Backed Assets During COVID-19 Pandemic," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 69(3), pages 417-433, August.
    3. Cevik, Emrah Ismail & Gunay, Samet & Zafar, Muhammad Wasif & Destek, Mehmet Akif & Bugan, Mehmet Fatih & Tuna, Fatih, 2022. "The impact of digital finance on the natural resource market: Evidence from DeFi, oil, and gold," Resources Policy, Elsevier, vol. 79(C).
    4. Jalan, Akanksha & Matkovskyy, Roman & Potì, Valerio, 2022. "Shall the winning last? A study of recent bubbles and persistence," Finance Research Letters, Elsevier, vol. 45(C).
    5. Zarifhonarvar, Ali, 2022. "The Effect of Covid Pandemic on Cryptocurrency Markets; A Literature Review," EconStor Preprints 266369, ZBW - Leibniz Information Centre for Economics.
    6. Katsiampa, Paraskevi & Yarovaya, Larisa & Zięba, Damian, 2022. "High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    7. Bentes, Sónia R., 2022. "On the stylized facts of precious metals’ volatility: A comparative analysis of pre- and during COVID-19 crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    8. Bruce Mizrach, 2022. "Stablecoins: Survivorship, Transactions Costs and Exchange Microstructure," Papers 2201.01392, arXiv.org, revised Feb 2023.
    9. Ren, Boru & Lucey, Brian, 2022. "A clean, green haven?—Examining the relationship between clean energy, clean and dirty cryptocurrencies," Energy Economics, Elsevier, vol. 109(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. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    2. Yarovaya, Larisa & Brzeszczyński, Janusz & Goodell, John W. & Lucey, Brian & Lau, Chi Keung Marco, 2022. "Rethinking financial contagion: Information transmission mechanism during the COVID-19 pandemic," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    3. Peng‐Fei Dai & John W. Goodell & Luu Duc Toan Huynh & Zhifeng Liu & Shaen Corbet, 2023. "Understanding the transmission of crash risk between cryptocurrency and equity markets," The Financial Review, Eastern Finance Association, vol. 58(3), pages 539-573, August.
    4. BRIK, Hatem & El OUAKDI, Jihene & FTITI, Zied, 2022. "Roles of stable versus nonstable cryptocurrencies in Bitcoin market dynamics," Research in International Business and Finance, Elsevier, vol. 62(C).
    5. Yarovaya, Larisa & Matkovskyy, Roman & Jalan, Akanksha, 2022. "The COVID-19 black swan crisis: Reaction and recovery of various financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    6. Li, Yi & Urquhart, Andrew & Wang, Pengfei & Zhang, Wei, 2021. "MAX momentum in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 77(C).
    7. Matkovskyy, Roman, 2019. "Centralized and decentralized bitcoin markets: Euro vs USD vs GBP," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 270-279.
    8. Łęt, Blanka & Sobański, Konrad & Świder, Wojciech & Włosik, Katarzyna, 2023. "What drives the popularity of stablecoins? Measuring the frequency dynamics of connectedness between volatile and stable cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    9. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    10. Conlon, Thomas & Corbet, Shaen & McGee, Richard J., 2020. "Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 54(C).
    11. Inekwe, John Nkwoma, 2020. "Liquidity connectedness and output synchronisation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 66(C).
    12. Cebiroglu, Gökhan & Hautsch, Nikolaus & Walsh, Christopher, 2019. "Revisiting the stealth trading hypothesis: Does time-varying liquidity explain the size-effect?," CFS Working Paper Series 625, Center for Financial Studies (CFS).
    13. Belkhir, Mohamed & Saad, Mohsen & Samet, Anis, 2020. "Stock extreme illiquidity and the cost of capital," Journal of Banking & Finance, Elsevier, vol. 112(C).
    14. Hu, Yang & Hou, Yang (Greg) & Oxley, Les & Corbet, Shaen, 2021. "Does blockchain patent-development influence Bitcoin risk?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    15. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Gabauer, David, 2019. "Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 37-51.
    16. Ahmed, Walid M.A., 2021. "How do Islamic equity markets respond to good and bad volatility of cryptocurrencies? The case of Bitcoin," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    17. Ruenzi, Stefan & Ungeheuer, Michael & Weigert, Florian, 2020. "Joint Extreme events in equity returns and liquidity and their cross-sectional pricing implications," Journal of Banking & Finance, Elsevier, vol. 115(C).
    18. French, Joseph J. & Taborda, Rodrigo, 2018. "Disentangling the relationship between liquidity and returns in Latin America," Global Finance Journal, Elsevier, vol. 36(C), pages 23-40.
    19. Mousumi Bhattacharya & Sharad Nath Bhattacharya & Sumit Kumar Jha, 2022. "Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data," Australian Journal of Management, Australian School of Business, vol. 47(2), pages 251-272, May.
    20. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).

    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:hal:journl:hal-03512893. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.