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Multiresolution analysis and spillovers of major cryptocurrency markets


  • Omane-Adjepong, Maurice
  • Alagidede, Imhotep Paul


The paper explores market coherencies and volatility causal linkages of seven leading cryptocurrencies for a sample period from August 8, 2014 to February 2, 2018. Wavelet-based methods are used to examine market connectedness. Parametric and nonparametric tests are employed to investigate direction of volatility spillovers of the assets. Highlights of our results indicate that: (1) probable diversification benefits are confined from intraweek to monthly scales for specific market pairs, and also for the investment basket having all seven assets; (2) incremental predictive power becomes useful in unveiling the nonlinear nature of volatility feedback linkages within time-scales; and (3) the level of connectedness and volatility causal linkages are found to be sensitive to trading scales and the proxy for market volatility. Based on these results, investors and risk managers are cautioned to incorporate such market dynamics in any adopted trading strategy for these asset markets. We believe our findings hold much relevance for portfolio diversification and risk management.

Suggested Citation

  • Omane-Adjepong, Maurice & Alagidede, Imhotep Paul, 2019. "Multiresolution analysis and spillovers of major cryptocurrency markets," Research in International Business and Finance, Elsevier, vol. 49(C), pages 191-206.
  • Handle: RePEc:eee:riibaf:v:49:y:2019:i:c:p:191-206
    DOI: 10.1016/j.ribaf.2019.03.003

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    Cited by:

    1. Peng Xie & Jiming Wu & Hongwei Du, 2019. "The relative importance of competition to contagion: evidence from the digital currency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-19, December.
    2. Qiao, Xingzhi & Zhu, Huiming & Hau, Liya, 2020. "Time-frequency co-movement of cryptocurrency return and volatility: Evidence from wavelet coherence analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    3. Qureshi, Saba & Aftab, Muhammad & Bouri, Elie & Saeed, Tareq, 2020. "Dynamic interdependence of cryptocurrency markets: An analysis across time and frequency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    4. Tran, Vu Le & Leirvik, Thomas, 2020. "Efficiency in the markets of crypto-currencies," Finance Research Letters, Elsevier, vol. 35(C).
    5. Duc Huynh, Toan Luu & Burggraf, Tobias & Wang, Mei, 2020. "Gold, platinum, and expected Bitcoin returns," Journal of Multinational Financial Management, Elsevier, vol. 56(C).
    6. Corbet, Shaen & Larkin, Charles & Lucey, Brian, 2020. "The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies," Finance Research Letters, Elsevier, vol. 35(C).
    7. Ferreira, Paulo & Kristoufek, Ladislav & Pereira, Eder Johnson de Area Leão, 2020. "DCCA and DMCA correlations of cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

    More about this item


    Cryptocurrency; Co-movement; Feedback linkages; MODWT; Diversification;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements
    • G1 - Financial Economics - - General Financial Markets


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