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用马尔可夫切换GARCH与随机模型预测比特币波动率

文章 arXiv papers · 作者: Dennis Koch et al.
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总结

本文使用马尔可夫切换GARCH和随机波动率模型（包括随机自回归波动率模型SARV）比较比特币日收益的条件方差预测。研究评估样本外预测表现，并建议分阶段估计均值方程和方差方程的系数。文中以此作为估计程序的理由。

报告结果显示，随机波动率模型，尤其是SARV，优于马尔可夫切换GARCH替代模型。结果还表明，具有持续性的标准GARCH模型有时比马尔可夫切换GARCH模型更能预测收益方差。这些比较为比特币风险预测中波动率模型的选择提供了证据，但所提供的摘要没有样本日期、预测期限、评分指标或排名背后的详细条件。因此，研究发现不能证明某个模型在所有市场状态或预测设定下都占优。

核心观点

  • 本研究比较马尔可夫切换GARCH与随机波动率模型对比特币波动率的样本外预测。
  • 研究将随机自回归波动率模型纳入随机波动率模型类别。
  • 论文建议采用分阶段估计程序，将均值系数与方差系数分开估计。
  • 报告的比较结果中，随机波动率模型，尤其是SARV，优于MS-GARCH。
  • 在比特币收益方差预测中，具有持续性的简单GARCH有时优于MS-GARCH。

标签

加密资产波动率统计学风险管理
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# Modelling and Predicting the Conditional Variance of Bitcoin Daily Returns: Comparsion of Markov Switching GARCH and SV Models









This paper introduces a unique and valuable research design aimed at analyzing Bitcoin price volatility. To achieve this, a range of models from the Markov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) model classes are considered and their out-of-sample forecasting performance is thoroughly examined. The paper provides insights into the rationale behind the recommendation for a two-stage estimation approach, emphasizing the separate estimation of coefficients in the mean and variance equations. The results presented in this paper indicate that Stochastic Volatility models, particularly SARV models, outperform MS-GARCH models in forecasting Bitcoin price volatility. Moreover, the study suggests that in certain situations, persistent simple GARCH models may even outperform Markov-Switching GARCH models in predicting the variance of Bitcoin log returns. These findings offer valuable guidance for risk management experts, highlighting the potential advantages of SARV models in managing and forecasting Bitcoin price volatility.

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。