Reconstructing cryptocurrency processes via Markov chains

TL;DR

This paper explores the use of Markov chains of varying orders to model and forecast the intra-day return dynamics of major cryptocurrencies, revealing improved prediction accuracy over random methods and investigating long-memory effects.

Contribution

It introduces the application of high-order Markov chains to cryptocurrency return processes and assesses their forecasting performance and long-memory properties.

Findings

Markov chain models outperform random predictions in forecasting cryptocurrency returns.

Higher-order Markov chains (up to order eight) improve forecast accuracy.

Evidence of long-memory components in cryptocurrency return processes.

Abstract

The growing attention on cryptocurrencies has led to increasing research on digital stock markets. Approaches and tools usually applied to characterize standard stocks have been applied to the digital ones. Among these tools is the identification of processes of market fluctuations. Being interesting stochastic processes, the usual statistical methods are appropriate tools for their reconstruction. There, besides chance, the description of a behavioural component shall be present whenever a deterministic pattern is ever found. Markov approaches are at the leading edge of this endeavour. In this paper, Markov chains of orders one to eight are considered as a way to forecast the dynamics of three major cryptocurrencies. It is accomplished using an empirical basis of intra-day returns. Besides forecasting, we investigate the existence of eventual long-memory components in each of those stochastic processes. Results show that predictions obtained from using the empirical probabilities are better than random choices.