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Exploring Dependence Relationships between Bitcoin and Commodity Returns: An Assessment Using the Gerber Cross-Correlation

Author

Listed:
  • Kokulo K. Lawuobahsumo

    (Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
    These authors contributed equally to this work.)

  • Bernardina Algieri

    (Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
    Department of Economic and Technological Change, Zentrum für Entwicklungsforschung (ZEF), Universität Bonn, Walter-Flex-Straße 3, 53113 Bonn, Germany
    These authors contributed equally to this work.)

  • Leonardo Iania

    (CORE/LFIN, Université catholique de Louvain (UCLouvain), Voie du Roman Pays 34, B-1348 Louvain-la-Neuve, Belgium
    Department Accounting, Finance and Insurance, University of Leuven (KU Leuven), Naamsestraat 69, 3001 Leuven, Belgium
    These authors contributed equally to this work.)

  • Arturo Leccadito

    (Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
    These authors contributed equally to this work.)

Abstract

We use a robust measure of non-linear dependence, the Gerber cross-correlation statistic, to study the cross-dependence between the returns on Bitcoin and a set of commodities, namely wheat, gold, platinum and crude oil WTI. The Gerber statistic enables us to obtain a more robust co-movement measure since it is neither affected by extremely large nor small movements that characterise financial time series; thus, it strips out noise from the data and allows us to capture effective co-movements between series when the movements are “substantial”. Focusing on the period 2014–2022, we construct the bootstrapped confidence intervals for the Gerber statistic and test the null that all the Gerber cross-correlations up to lag k max are zero. Our results indicate a low degree of dependence between Bitcoin and commodities prices, both when we consider contemporaneous correlation and when we employ correlations between current Bitcoin and lagged (one day, one week, or one month) commodities returns. Further, the cross-correlation between Bitcoin and commodities’ returns, although scanty, shows an increasing trend during periods of economic, health and financial turbulence. This increased cross-correlation of returns during hectic market periods could be due to the contagion effect of some markets by others, which could also explain the strong dependence across volatilities we detected. Based on our results, Bitcoin cannot be considered the “new digital gold”.

Suggested Citation

  • Kokulo K. Lawuobahsumo & Bernardina Algieri & Leonardo Iania & Arturo Leccadito, 2022. "Exploring Dependence Relationships between Bitcoin and Commodity Returns: An Assessment Using the Gerber Cross-Correlation," Commodities, MDPI, vol. 1(1), pages 1-16, August.
    • Handle: RePEc:gam:jcommo:v:1:y:2022:i:1:p:4-49:d:897543
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    References listed on IDEAS

    as
    1. Bernardina Algieri & Arturo Leccadito & Pietro Toscano, 2021. "A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    2. Aloui, Riadh & Aïssa, Mohamed Safouane Ben & Nguyen, Duc Khuong, 2011. "Global financial crisis, extreme interdependences, and contagion effects: The role of economic structure?," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 130-141, January.
    3. Bouri, Elie & Gupta, Rangan & Lahiani, Amine & Shahbaz, Muhammad, 2018. "Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices," Resources Policy, Elsevier, vol. 57(C), pages 224-235.
    4. 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.
    5. Algieri, Bernardina & Leccadito, Arturo, 2019. "Ask CARL: Forecasting tail probabilities for energy commodities," Energy Economics, Elsevier, vol. 84(C).
    6. Hachmi Ben Ameur & Zied Ftiti & Waël Louhichi, 2022. "Revisiting the relationship between spot and futures markets: evidence from commodity markets and NARDL framework," Annals of Operations Research, Springer, vol. 313(1), pages 171-189, June.
    7. Algieri, Bernardina & Leccadito, Arturo, 2017. "Assessing contagion risk from energy and non-energy commodity markets," Energy Economics, Elsevier, vol. 62(C), pages 312-322.
    8. Frode Kjærland & Aras Khazal & Erlend A. Krogstad & Frans B. G. Nordstrøm & Are Oust, 2018. "An Analysis of Bitcoin’s Price Dynamics," JRFM, MDPI, vol. 11(4), pages 1-18, October.
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    Cited by:

    1. Kokulo K. Lawuobahsumo & Bernardina Algieri & Arturo Leccadito, 2024. "Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions?," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(3), pages 2647-2675, June.
    2. Atik, Zehra & Guven, Murat & Guloglu, Bulent & Koksalmis, Gulsah Hancerliogullari & Calisir, Fethi, 2025. "Exploring nonlinear tail dependencies: Cryptocurrencies, stablecoins, and commodity markets amid monetary shifts," Research in International Business and Finance, Elsevier, vol. 76(C).

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