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比较历史波动率、GARCH 波动率与比特币隐含波动率

文章 arXiv papers · 作者: Cristina Chinazzo et al.
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总结

本文比较刻画比特币波动率的三种方法：以样本标准差衡量的历史波动率、GARCH 类模型的条件预测，以及从比特币期权推导的隐含波动率。研究利用这些指标考察观测到的波动、模型预期与期权市场定价之间的关系。研究报告称，在日度和年度期限上，三种方法都显示预期波动率较高，但摘录没有给出具体数值或评估期间。

作者提醒，期权市场流动性有限。这可能降低隐含波动率的可靠性，尤其是对深度虚值或深度实值、或期限极端的期权而言，并可能使其与其他指标不同。该比较有助于说明每种指标可能传递的不同信息，但摘录没有说明预测准确性检验、模型细节，或这些指标相对于已实现波动率的表现。因此，结论仅具描述性；解读隐含波动率发现时应考虑文中所述的流动性限制。

核心观点

  • 历史波动率、GARCH 类预测和期权隐含波动率反映了比特币风险的不同视角。
  • 研究报告称，所考察的各种方法均显示预期波动率较高。
  • 比特币期权流动性有限，可能使隐含波动率估算失真。
  • 对于虚实值程度或期限极端的期权，隐含波动率估算可能尤其不可靠。

标签

加密资产波动率期权统计学
全文
# Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches


# Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches









This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's crucial to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme moneyness or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。