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

TL;DR

This paper introduces an adaptive multiscale analysis method combining ensemble empirical mode decomposition and Hilbert spectral analysis to study the complex, time-varying dynamics of cryptocurrency prices.

Contribution

It develops an adaptive approach for decomposing cryptocurrency prices into intrinsic modes and analyzing their energy-frequency characteristics.

Findings

Reveals multiscale properties of cryptocurrency price dynamics

Provides instantaneous energy-frequency spectra of cryptocurrencies

Demonstrates adaptive decomposition captures volatility changes

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.