A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes

Alessio Brini, Jimmie Lenz
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Abstract

The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies' future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.
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加密货币波动性比较--以新资产类别和成熟资产类别为基准
本文从总量和个体两个层面分析了加密货币生态系统,以了解影响未来波动性的因素。该研究使用 2020 年至 2022 年的高频面板数据,研究了几个市场波动驱动因素之间的关系,如每日杠杆率、签名波动率和跳跃率。对不同市场制度下的几个已知自回归模型规格进行了估计,并将结果与股票数据进行了比较,作为更成熟资产类别的参考基准。面板估计结果表明,高频水平的正市场回报增加了价格波动性,这与经典金融文献的预期相反。我们通过在不同时间跨度上重复估计,将这种影响归因于数据集最后一年(2022 年)的价格动态。此外,正的签名波动率和负的日杠杆率对加密货币的未来波动率产生了积极影响,这与对股票横截面的同一研究结果不同。这一结果预示着新生加密货币市场的结构性差异,该市场尚待成熟。进一步的个人层面分析证实了面板分析的结果,并强调这些影响在统计上是显著的,而且在所选范围内的许多成分中是共有的。
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