Abstract
The growing cryptocurrency market has attracted the attention from many investors worldwide, mainly due to the ease of entering the market and its extremely volatile character. The main aim of this paper is to examine interdependencies between log-returns of cryptocurrencies, with the special focus on Bitcoin. Based on implications from the literature, we use methods dedicated for studying the stock market and apply the two-step analysis, comparing results between two subsequent periods. Results obtained using Minimum Spanning Tree (MST) method show that cryptocurrencies form hierarchical clusters, consistently over two separate periods, indicating potential topological properties of the cryptocurrency market. Then, using Vector Autoregression (VAR) model, we study the transmission of demand shocks within clusters. Results show that changes in Bitcoin price do not affect and are not affected by changes in prices of other cryptocurrencies. However, results indicate that findings obtained for Bitcoin shall not be generalized to the entire cryptocurrency market.
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