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Volatility Spillover and Directionality in Cryptocurrency and Metal Markets

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  • Sumanjay Dutta
  • Parthajit Kayal
  • G. Balasubramnaian

Abstract

This article investigates the dynamic relationship between cryptocurrencies and metals, examining the existence and direction of volatility spillovers. While previous studies have explored the relationships between different cryptocurrencies and between base metals and gold, there is a notable gap in understanding the volatility spillover nexus among cryptocurrencies. This study makes a significant contribution by employing the Time-Varying-Parameter-Vector-Autoregressive (TVP-VAR) total connectedness measure to assess the strength of association between these assets. Our analysis employs 10-year daily returns data for three cryptocurrencies (Bitcoin, Litecoin, and Ethereum) and two metals (Gold and Copper). As we witness major economic events worldwide, this study is particularly relevant, as it provides insights into potential hedging opportunities. To comprehend the risk contagion patterns, various measures of partial and dynamic connectedness are computed, supporting the earlier TVP-VAR analysis. The findings indicate that Litecoin and Ethereum exhibit a high level of connectedness, while Bitcoin remains relatively less connected. Among the metals, Gold and Copper demonstrate similar levels of connectedness in certain cases. Notably, there is a significant risk contagion between Litecoin and metals. These results hold essential implications for policy-makers and portfolio managers with different time horizons, offering valuable insights into risk contagion within the cryptocurrency and metal markets. JEL Codes: C32; G15; G17; G41

Suggested Citation

  • Sumanjay Dutta & Parthajit Kayal & G. Balasubramnaian, 2023. "Volatility Spillover and Directionality in Cryptocurrency and Metal Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 22(4), pages 464-485, December.
    • Handle: RePEc:sae:emffin:v:22:y:2023:i:4:p:464-485
    DOI: 10.1177/09726527231192143
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    Cited by:

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    2. John Kingsley Woode & Peterson Owusu Junior & Anthony Adu-Asare Idun & Seyram Kawor & John Bambir & Anokye M. Adam, 2025. "Time-Varying and Frequency-Based Spillover Connectedness Between Cryptocurrencies and Non-ferrous Industrial Metals in Light of Market Plummets," Computational Economics, Springer;Society for Computational Economics, vol. 66(3), pages 2225-2264, September.
    3. Hussain, Sabbor & Chen, Jo-Hui, 2025. "The return and volatility spillovers among decentralized finance (DeFi) assets," Research in International Business and Finance, Elsevier, vol. 79(C).
    4. Florin Aliu & Artor Nuhiu, 2025. "Analyzing the Interconnectedness Within the Volatile Crypto Market: Evidence from Two Consequent Non-economic Shocks," SAGE Open, , vol. 15(2), pages 21582440251, June.
    5. Moni, M. & Sreeraj, & Sankararaman, S., 2025. "Unveiling the interdependency of cryptocurrency and Indian stocks through wavelet and nonlinear time series analysis: An Econophysics approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 670(C).
    6. Hassan Javed & Naveed Khan, 2025. "Do Bitcoin Shocks Dominate Other Cryptocurrencies? An Examination Through GARCH Based Dynamic Models," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 32(4), pages 1431-1457, December.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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