Market dynamics, cyclical patterns and market states

Purpose: In this paper, the authors seek to investigate the dynamics of Bitcoin, Litecoin, Ethereum and Ripple daily returns and volatilities. Design/methodology/approach: In this paper, the authors apply the MS-ARMA model on daily returns of Bitcoin (19/04/2013-13/02/2018), Ripple (05/08/2013-14/02/2018), Litcoin (29/04/2013-14/02/2018) and Ethereum (08/02/2015-14/02/2018). This model allows capture of the nonlinear structure in both the conditional mean and the conditional variance of cryptocurrency returns. Findings: All the cryptocurrency markets show regime switching in the return-generating process. Market dynamics seem to be governed by two different states which differ from one cryptocurrency market to another in terms of mean return, volatility and interstate dynamics. These findings can be explained by investors’ behavior, i.e. speculative trading and herding behavior. By choosing to participate (or imitating some investors) in some cryptocurrency markets (in particular Bitcoin market), they affect the price movements and therefore the market dynamics in the short run. Practical implications: Identifying the different market states provides information for investors to make more accurate portfolio decisions in the virtual market and follow the market timing strategy. Originality/value: This paper attempts to analyze potential nonlinear structure in cryptocurrencies returns and analyze if there is a difference between the cryptocurrencies market cycles. So, the search for congruent and adequate specification to reproduce the stock returns dynamics in the virtual market still remains the concern of several empirical studies. This research not only examines the behavior of stock returns in the cryptocurrencies’ market but also highlights the existence of nonlinearity propriety as a stylized fact.