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Return-Volatility Nexus in the Digital Asset Class: A Dynamic Multilayer Connectedness Analysis

Author

Listed:
  • Elie Bouri

    (Adnan Kassar School of Business, Lebanese American University, Lebanon)

  • Matteo Foglia

    (Department of Economics and Finance, University of Bari “Aldo Moro†, Italy)

  • Sayar Karmakar

    (Department of Statistics, University of Florida, USA)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

Based on the rationale that returns and volatility are interrelated, we apply a multilayer network framework involving the return layer and volatility layer of cryptocurrencies, NFTs, and DeFi assets over the period January 1, 2018 - January 23, 2024. The results show significant connectedness in each of the return and volatility layers, with major cryptocurrencies such as Bitcoin and Ethereum playing a central role. Large spikes in the level of connectedness are noticed around COVID-19 pandemic and Russia-Ukraine conflict, and Bitcoin and Ethereum emerge are net transmitters of returns and volatility shocks, emphasizing their significant role around these crisis periods. Notably, a strong positive rank correlation exists between the return and volatility layers, highlighting the significant risk-return relationship in the digital asset class. The findings suggest that economic actors should not ignore the interconnectedness between the return and volatility layers in the system of cryptocurrencies, NFTs, and DeFi assets for the sake of a comprehensive analysis of information flow. Otherwise, a share of the information flow concerning the return-volatility nexus across these digital assets would be missed, possibly leading to inferences regarding asset pricing, portfolio allocation, and risk management.

Suggested Citation

  • Elie Bouri & Matteo Foglia & Sayar Karmakar & Rangan Gupta, 2024. "Return-Volatility Nexus in the Digital Asset Class: A Dynamic Multilayer Connectedness Analysis," Working Papers 202432, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202432
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    More about this item

    Keywords

    Multilayer networks; Spillover effects; return-volatility; cryptocurrencies; NFTs; DeFi; COVID-19; Russia-Ukraine conflict;
    All these keywords.

    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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