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Leverage effect in cryptocurrency markets

Author

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  • Huang, Jing-Zhi
  • Ni, Jun
  • Xu, Li

Abstract

In this article we study the leverage effect in cryptocurrency markets using a stochastic volatility model with simultaneous and correlated jumps in returns and volatility. We estimate the model using an efficient sequential learning algorithm with daily data on four actively traded cryptocurrencies including Bitcoin, Ethereum, Chainlink, and Litecoin. Doing so allows us to sequentially learn about the return-volatility relationships and the leverage effect in these cryptocurrencies when new data come in. We find that these relationships depend on both the diffusive and jump components of correlations between returns and volatility. Interestingly, the diffusive and jump components often have opposite signs for these currencies; that is, while the diffusive component may exhibit a negative return-volatility relationship (the “leverage effect”), the jump component may show a positive relationship (the “inverse leverage effect”). As a result, the total leverage effect can be quite different from the diffusive leverage effect, due to the presence of correlated jumps in returns and volatility. Overall, we provide evidence that these jumps matter greatly to the total leverage effect in cryptocurrency markets.

Suggested Citation

  • Huang, Jing-Zhi & Ni, Jun & Xu, Li, 2022. "Leverage effect in cryptocurrency markets," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:pacfin:v:73:y:2022:i:c:s0927538x22000683
    DOI: 10.1016/j.pacfin.2022.101773
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    Cited by:

    1. Silky Vigg Kushwah & Shab Hundal & Payal Goel, 2024. "Unveiling Interconnectedness and Volatility Transmission: A Novel GARCH Analysis of Leading Global Cryptocurrencies," International Journal of Economics and Financial Issues, Econjournals, vol. 14(3), pages 132-139, May.
    2. Wujun Lv & Tao Pang & Xiaobao Xia & Jingzhou Yan, 2023. "Dynamic portfolio choice with uncertain rare-events risk in stock and cryptocurrency markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    3. Alessio Brini & Jimmie Lenz, 2024. "A comparison of cryptocurrency volatility-benchmarking new and mature asset classes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-38, December.
    4. Boyi Li & Weixuan Xia, 2024. "Crypto Inverse-Power Options and Fractional Stochastic Volatility," Papers 2403.16006, arXiv.org, revised Sep 2024.
    5. Zhang, Chuanhai & Ma, Huan & Liao, Xiaosai, 2023. "Futures trading activity and the jump risk of spot market: Evidence from the bitcoin market," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    6. Khaki, Audil & Prasad, Mason & Al-Mohamad, Somar & Bakry, Walid & Vo, Xuan Vinh, 2023. "Re-evaluating portfolio diversification and design using cryptocurrencies: Are decentralized cryptocurrencies enough?," Research in International Business and Finance, Elsevier, vol. 64(C).
    7. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.

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    More about this item

    Keywords

    Leverage effect; Cryptocurrency; Sequential learning; Stochastic volatility; Simultaneous and correlated jumps; Particle filters;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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