Wavelet Analysis of Cryptocurrencies -- Non-Linear Dynamics in High Frequency Domains

TL;DR

This paper applies wavelet analysis to cryptocurrency prices to explore non-linear dynamics and cyclical persistence across different frequencies, providing insights into market efficiency and intrinsic causal relationships.

Contribution

It demonstrates the effectiveness of wavelet analysis in detecting non-linear dynamics and cyclical persistence in high-frequency cryptocurrency data, challenging the efficient market hypothesis.

Findings

Detection of cyclical persistence at multiple frequencies

Evidence of non-linear dynamics in high-frequency data

Insights into market efficiency and causal relationships

Abstract

In this study, we perform some analysis for the probability distributions in the space of frequency and time variables. However, in the domain of high frequencies, it behaves in such a way as the highly non-linear dynamics. The wavelet analysis is a powerful tool to perform such analysis in order to search for the characteristics of frequency variations over time for the prices of major cryptocurrencies. In fact, the wavelet analysis is found to be quite useful as it examine the validity of the efficient market hypothesis in the weak form, especially for the presence of the cyclical persistence at different frequencies. If we could find some cyclical persistence at different frequencies, that means that there exist some intrinsic causal relationship for some given investment horizons defined by some chosen sampling scales. This is one of the characteristic results of the wavelet analysis in the time-frequency domains.