Predicting cryptocurrency volatility is crucial for investors, traders, and decision-makers but is complicated by the market’s high non-linearity, volatility, and noise. This paper presents a novel approach, the CEEMDAN-RF-LSTM hybrid model, which is the first to combine the strengths of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Random Forest (RF), and Long Short-Term Memory Network (LSTM) to predict the Realized Volatility (RV) of mainstream cryptocurrencies. The model exploits CEEMDAN’s proficiency in processing non-linear and non-stationary signals, RF’s exceptional feature selection capabilities, and LSTM’s distinctive advantages in dealing with time-series problems. Applied to actual transaction data for Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB), empirical results show the superior performance of our model in predicting actual cryptocurrency volatility. These findings contribute to the academic understanding of cryptocurrency volatility and provide practical guidance for quantitative trading strategy development, offering fresh insights and methodologies for related research fields.

中文翻译：

---

使用 CEEMDAN-RF-LSTM 估计加密货币的实际波动率


预测加密货币的波动性对于投资者、交易者和决策者来说至关重要，但由于市场的高度非线性、波动性和噪音而变得复杂。本文提出了一种新颖的方法，即 CEEMDAN-RF-LSTM 混合模型，该模型是第一个将自适应噪声完全集成经验模式分解 (CEEMDAN)、随机森林 (RF) 和长短期记忆网络的优点结合起来的方法（LSTM）来预测主流加密货币的实际波动率（RV）。该模型利用了CEEMDAN在处理非线性和非平稳信号方面的熟练程度、RF卓越的特征选择能力以及LSTM在处理时间序列问题方面的独特优势。应用于比特币（BTC）、以太坊（ETH）和币安币（BNB）的实际交易数据，实证结果表明我们的模型在预测实际加密货币波动性方面具有优越的性能。这些发现有助于学术界对加密货币波动性的理解，并为量化交易策略的开发提供实践指导，为相关研究领域提供新的见解和方法论。