Unravelling the volume-volatility nexus in cryptos under structural breaks using fat-tailed distributions: mixture of distribution hypothesis and implications for market efficiency

Abstract

The study examines the relationship between volume and volatility in leading cryptocurrencies i.e. Bitcoin and Ethereum, within the framework of Mixture of Distribution Hypothesis (MDH). It accommodates structural shifts in the cryptocurrency prices and uses fat-tailed distributions. The results show that the MDH is rejected for both cryptocurrencies, and volume alone cannot explain the heteroskedasticity of returns; however, it acts as a significant predictor for volatility, especially when incorporating structural breaks in the model. Further, the forecasting performance improves when fat-tailed distributions, such as the skewed student’s t and Johnson’s Su distribution are used to model the innovations. Thus, volume holds important information in the crypto markets and can affect returns, thereby, raising concerns about market efficiency. Our results are robust across different periods, modelling approaches and forecasting horizons, and hold substantial implications for traders, market participants, regulators, and governments in designing effective policies.