数字加密货币
杠杆效应
杠杆(统计)
波动性(金融)
计量经济学
跳跃
经济
已实现方差
货币经济学
金融经济学
ARCH模型
计算机科学
数学
统计
物理
计算机安全
量子力学
作者
Jing‐Zhi Huang,Jun Ni,Li Xu
标识
DOI:10.1016/j.pacfin.2022.101773
摘要
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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