Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices

20 Pages Posted: 1 May 2021 Last revised: 17 May 2021

See all articles by Tim Leung

Tim Leung

University of Washington - Department of Applied Math

Theodore Zhao

University of Washington, Dept. of Applied Mathematics

Date Written: May 17, 2021

Abstract

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: cryptocurrency, time series, empirical mode decomposition, Hilbert spectral analysis

JEL Classification: G17, B23, B26

Suggested Citation

Leung, Tim and Zhao, Zhengde, Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices (May 17, 2021). Available at SSRN: https://ssrn.com/abstract=3833262 or http://dx.doi.org/10.2139/ssrn.3833262

Tim Leung

University of Washington - Department of Applied Math ( email )

Lewis Hall 217
Department of Applied Math
Seattle, WA 98195
United States

HOME PAGE: http://faculty.washington.edu/timleung/

Zhengde Zhao (Contact Author)

University of Washington, Dept. of Applied Mathematics ( email )

Seattle, WA
United States

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