Adaptive complementary ensemble EMD and energy-frequency spectra of cryptocurrency prices

Tim Leung and Theodore Zhao ()
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Theodore Zhao: Applied Mathematics Department, University of Washington, Seattle, WA 98195, USA

International Journal of Financial Engineering (IJFE), 2022, vol. 09, issue 01, 1-23

Abstract: In this study, we study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.

Keywords: Time series; empirical mode decomposition; spectral analysis; filtering; volatility; cryptocurrency (search for similar items in EconPapers)
Date: 2022
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Working Paper: Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices (2021)
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DOI: 10.1142/S2424786321410085

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