Extreme return-volume relationship in cryptocurrencies: Tail dependence analysis

Faisal Mustafa,Muhammad Naeem,Sheraz Ahmed,Papavassiliou Vassilios,Kashif Saleem,Naeem Muhammad

doi:10.1080/23322039.2020.1834175

Abstract

We explore extreme return-volumes dependence among different cryptocurrencies such as Bitcoin, Ethereum, Ripple, and Litecoin by using the Copula approach. We use Student-t, Frank, Clayton, Survival Clayton, Gumbel, and SJC copulas. We filter out margins by using the EGARCH model for return series and GARCH model for volume series. Evidence of significant symmetric dependence between return-volume is not found due to insignificance of student-t and Frank copula parameters. In a return-volume relationship, coefficients of lower tail dependence are significant for Bitcoin, Ripple, and Litecoin which means that low returns are followed by low volumes. Lower tail dependence for the return-volume relationship is stronger than the upper tail dependence for Bitcoin, Ripple, and Litecoin. Moreover, for negative return-volume, left tail dependence coefficients are significant for Ripple and Litecoin, which means that high returns are followed by low volumes for Ripple and Litecoin. Our investigation shows that investors (buyer or seller) are very careful in extreme market conditions for both Ripple and Litecoin. Extreme upper tail and lower tail dependence coefficients are insignificant for Ethereum.

Highlights

One of the most significant innovations in finance has been the creation and development of cryptocurrencies
This paper investigates extremely high or low returns on trading volumes for leading crypto currencies.Extreme return-volumes depen dence among different cryptocurrencies by employing Copula methodology.Investigation shows that investors are very careful in extreme market conditions for both Ripple and Litecoin.Finding is unique to the cryptocurrency market, which might be due to the highly volatile nature of cryptocurrencies
We filter out margins by using the Exponential GARCH (EGARCH) model for return series and GARCH model for volume series and utilizes them in different copulas in order to measure dependence

Summary

Introduction

One of the most significant innovations in finance has been the creation and development of cryptocurrencies. Except in Bitcoin bear and bull market regimes They suggested that this result highlights the importance of modeling nonlinearity and accounting for the tail behavior when analyzing causal relationships between Bitcoin returns and trading volume. Tiwari et al (2020) examine the dependence and contagion risk between Bitcoin (BTC), Litecoin (LTC), and Ripple (XRP) using non-parametric mixture copulas Their results provide evidence of significant risk contagion among price returns of major cryptocurrencies, both in bull and bear markets. Naeem et al (2020) analyze the average and extreme dependence between returns and trading volumes of three main cryptocurrencies (Bitcoin, Ethereum, and Litecoin) by time-varying copula methodology. Section four reports empirical results and section five concludes with implications, limitations, and future work recommendations

EGARCH model

À ρ2 À 1 À 1

Clayton copula The Clayton copula has the following form:

Frank copula The Frank copula is defined by:

Empirical studies and analysis

Findings

Conclusion and implications