Covid-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent

TL;DR

This study investigates how the Covid-19 pandemic affected the long-term memory and volatility of cryptocurrencies using wavelet-based Hurst exponent analysis on high-frequency data.

Contribution

It applies a novel wavelet transform method to analyze the impact of Covid-19 on the long-range dependence in cryptocurrency returns and volatility.

Findings

Long memory in returns was only mildly affected during Covid-19 peak.

Volatility experienced a temporary change in its long-range correlation structure.

Results provide insights for investors and researchers on cryptocurrency behavior during crises.

Abstract

Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into complex, high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of eleven important coins. Our study covers the pre-Covid-19 and the subsequent pandemia period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.