TY - JOUR
T1 - Leverage effect in cryptocurrency markets
AU - Huang, Jing Zhi
AU - Ni, Jun
AU - Xu, Li
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
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U2 - 10.1016/j.pacfin.2022.101773
DO - 10.1016/j.pacfin.2022.101773
M3 - Article
AN - SCOPUS:85130889789
SN - 0927-538X
VL - 73
JO - Pacific Basin Finance Journal
JF - Pacific Basin Finance Journal
M1 - 101773
ER -