探索加密货币波动率预测中的模型不确定性问题:log转换、时区采样以及模型设定

Exploring Model Uncertainty in Cryptocurrency Volatility Prediction:log-Transformation,Time-Zone Sampling,and Model Specification

文章全面地比较了一系列模型在预测加密货币波动率时的表现.结果发现,粗糙波动率模型在预测多期样本外的波动率时表现更加稳健和可靠,而异质自回归(HAR)模型相对较弱,但经过log转换后的HAR模型在预测上则表现更优.此外,考虑到加密货币的特点,选取合适的时区划分依据也非常重要,因为不同的时区可能对加密货币市场的波动率产生影响.研究还引入了最小二乘模型平均法来应对波动率建模中的模型不确定性.结果表明,模型平均方法在加密货币市场波动率预测中相比其他方法具有优越性,能够平衡不同模型之间的优缺点,提高预测的可信度和稳定性,对于预测市场的波动性是非常有效的.文章研究指出,在选择合适的波动率模型时需要综合考虑加密货币波动率的特性和历史表现,并且在应用模型时需要注意其在不同数据集和预测目标下的表现,避免盲目使用导致预测效果的不确定性.

This paper presents a comprehensive comparison of various models for predicting cryptocurrency volatility.The findings highlight that the rough volatility model demonstrates robust and reliable performance in forecasting out-of-sample volatility acrossmultiple periods.Conversely,the heterogeneous autoregressive(HAR)model shows relatively weaker results.However,the log-transformed HAR model exhibits superior predictive capabilities.Additionally,the study emphasizes the significance of selecting appropriate time zone divisions,considering the impact of different time zones on cryptocurrency market volatility.To address model uncertainty in volatility modeling,the paper introduces the method of model averaging using least squares.The results indicate that model averaging outperforms alternative approaches by effectively balancing the strengths and weaknesses of different models,ultimately enhancing the credibility and stability of predictions in the cryptocurrency market.The study underscores the importance of considering the unique characteristics and historical performance of cryptocurrency volatility when selecting suitable volatility models.Furthermore,it emphasizes the need for careful evaluation of model performance across diverse datasets and prediction targets to mitigate uncertainty arising from blind application.