Leverage effect in cryptocurrency markets

Jingzhi Huang, Jun Ni and Li Xu

Pacific-Basin Finance Journal, 2022, vol. 73, issue C

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.

Keywords: Leverage effect; Cryptocurrency; Sequential learning; Stochastic volatility; Simultaneous and correlated jumps; Particle filters (search for similar items in EconPapers)
JEL-codes: C11 C15 C58 G11 G17 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:73:y:2022:i:c:s0927538x22000683

DOI: 10.1016/j.pacfin.2022.101773

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