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• This study uses the PSY method to track Bitcoin and its forked coins ' price bubbles. • Bitcoin price bubbles are more common and last longer, while some forked coins do not have bubbles. • The correlation between the price bubble of Bitcoin and several forked coins is significant. • BCH and BTC are the closest, while BSV is the farthest from BTC. This study employs the PSY method to detect the price bubble of Bitcoin (BTC) and its forked coins. The statistical relationship between BTC and its forked coins is then calculated using Pearson correlation coefficients and six distance measurement methods. The findings indicate that (1) BTC has more bubbles with longer periods. In contrast, certain forked coins do not have bubbles; (2) The correlation between the price bubble of BTC and several forked coins is significant; (3) From the distance of binary time series, Bitcoin Cash and BTC are the closest, while Bitcoin SV is the farthest from BTC. [ABSTRACT FROM AUTHOR]

Abstract Concerned by the volatility of Bitcoin price growth (BPG), Bank Indonesia—Indonesia's central bank—discourages trading cryptocurrencies. We examine the relationship between Bitcoin price growth (BPG) and Indonesia's monetary aggregates (inflation, real exchange rate, and money velocity). In doing so, we develop the conceptual link between Bitcoin and monetary aggregates. We find strong and robust evidence that BPG leads to inflation growth, currency appreciation, and a reduction in money velocity. Our results have policy implications for other central banks in terms of achieving stability of the monetary system if BPG is indeed a concern for those countries. Highlights • We test whether Bitcoin price growth (BPG) influences Indonesia's monetary aggregates. • We develop the conceptual link between BPG and monetary aggregates. • Robust evidence is found that BPG leads to inflation growth. • BPG contributes to currency appreciation, and a reduction in money velocity. • Results have policy implications for other central banks in terms of achieving stability. [ABSTRACT FROM AUTHOR]

Inconsistency of consensus results in blockchain forks, which create a new financial risk. After filtering out Bitcoin's linear, nonlinear, and lag impacts on forked coins, this study employs a bottom-up hierarchical clustering algorithm to examine the logarithmic return series for Bitcoin and its 14 forked coins from 2018 to 2021. The results indicate that the market for forked coins can be divided into three clusters: SegWit-supported forked coins, mature forked coins, and the latest forked coins. Bitcoin and the mature forked coins form a cluster, and its performance is superior to others. Although Bitcoin's return significantly affects that of its forked coins, it does not affect the market structure. Furthermore, this study provides references for risk aversion among investors in forked coins and presents macro-level information for cryptocurrency market authorities. [Display omitted] • This study examines the market structure of Bitcoin and its forked coins. • We use the bottom-up hierarchical clustering algorithm to divide Bitcoin and its forked coins into three clusters. • Although Bitcoin has a substantial effect on the returns of forked coins, it has no effect on their market structure. • We address how to avoid the potential financial dangers associated with forked coins. [ABSTRACT FROM AUTHOR]