摘要
综述了经验模态分解方法的理论基础及其演进过程,从初始的EMD到其改进版本包括集合经验模态分解(EEMD)、完全集合经验模态分解(CEEMD)及结合噪声自适应方法(CEEMDAN)。这些方法通过逐步引入噪声辅助、集合平均和自适应噪声控制等技术,克服了原始EMD在端点效应和模式混叠等方面的不足,提高了分解的稳定性和精确性。此外,还探讨了EMD及其改进方法在金融领域的广泛应用,特别是在金融资产价格的预测、金融资产定价机制、金融风险管理等方面的实际进展。通过EMD及其衍生方法,学者能够有效地分解金融时间序列数据,揭示不同时间尺度上的市场波动特征,识别资产价格形成的非线性趋势和波动性聚集现象。这些应用不仅提升了金融资产价格预测的准确性和稳健性,还为复杂金融市场的风险评估和管理提供了更为细致的分析工具。研究结果表明,随着EMD方法的不断改进和成熟,其在金融领域的应用前景将更加广阔,并为未来的金融市场研究提供了强有力的工具和方法支持。
This paper reviews the theoretical foundation and evolution of the Empirical Mode Decomposition(EMD)method,tracing its development from the initial EMD to its advanced versions including Ensemble Empirical Mode Decomposition(EEMD),Complete Ensemble Empirical Mode Decomposition(CEEMD),and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).These methods progressively introduce noise-assisted,ensemble averaging,and adaptive noise control techniques,overcoming the limitations of the original EMD in aspects such as endpoint effects and mode mixing,thereby enhancing the stability and accuracy of the decomposition.Furthermore,this paper explores the extensive applications of EMD and its improved methods in the financial sector,particularly in the prediction of financial asset prices,financial asset pricing mechanisms,and financial risk management.Through EMD and its derivative methods,scholars can effectively decompose financial time series data,revealing market volatility characteristics at different time scales and identifying nonlinear trends and volatility clustering in asset price formation.These applications not only enhance the accuracy and robustness of financial asset price predictions but also provide more detailed analytical tools for risk assessment and management in complex financial markets.The findings of this study suggest that with the continuous improvement and maturation of EMD methods,their application prospects in the financial field will become broader,providing robust tools and methodological support for future financial market research.
作者
杨铠
杨玲玲
YANG Kai;YANG Lingling(School of Economics,Yunnan Normal University,Kunming Yunnan 650000,China)
出处
《金融理论与教学》
2024年第4期45-53,共9页
Financial Theory and Teaching
关键词
经验模态分解
价格预测
定价机制
风险管理
Empirical Mode Decomposition
price prediction
pricing mechanism
risk management
