比特币日收益率条件方差的建模与预测：Markov Switching GARCH与SV模型的比较
Modelling and Predicting the Conditional Variance of Bitcoin Daily Returns: Comparsion of Markov Switching GARCH and SV Models
AI总结：
本文比较了MS-GARCH与SARV等模型对比特币日收益率条件方差的样本外预测表现，发现SARV模型整体优于MS-GARCH，为风险管理提供了指导。
摘要：
本文介绍了一种独特且有价值的研究设计，旨在分析比特币价格波动率。为此，考虑了来自Markov Switching-GARCH和随机自回归波动率（SARV）模型类的一系列模型，并彻底检验了它们的样本外预测表现。本文提供了关于推荐两阶段估计方法背后原理的见解，强调了均值方程和方差方程中系数的分别估计。本文提出的结果表明，随机波动率模型，特别是SARV模型，在预测比特币价格波动率方面优于MS-GARCH模型。此外，研究表明，在某些情况下，持续的简单GARCH模型在预测比特币对数收益率方差时甚至可能优于Markov-Switching GARCH模型。这些发现为风险管理专家提供了有价值的指导，突出了SARV模型在管理和预测比特币价格波动率方面的潜在优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
