Empirical Mode Decomposition:Theoretical Development and Financial Empirical Progress
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.
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