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Bitcoin has recently become the focal point of investors as a digital currency and an alternative payment method. Despite Bitcoin being in the spotlight, a gap in the literature on its price-setting behaviors has been observed. This study aims to contribute to the literature by investigating the relationship between Bitcoin price and volume in the period between January 1, 2012 and April 7, 2018 through a symmetric and asymmetric causality test.

Daily price and volume data relevant to Bitcoin traded in the Bitstamp market were obtained from www.bitcoincharts.com. Within the framework of data applicable for analysis, the data set for this study includes a total of 2,286 observations for the period between January 1, 2012 and April 7, 2018.

Based on the results of the standard causality test, a causality relationship was determined from price to volume. Based on the results of the asymmetric causality test between positive and negative shocks of variables, a unilateral causality relationship was determined from negative shocks in Bitcoin prices to negative shocks in trading volume as well as from positive shocks in trading volume to positive shocks in prices. Furthermore, it was found that the relationship between Bitcoin price and volume is cointegrated.

The empirical results can be used by investors and portfolio managers to make trading decisions.

The contribution of this paper to the literature is that it is the first study on the symmetric and asymmetric causality relationship between Bitcoin price and volume. Moreover, this paper reveals short- and long-term behaviors of Bitcoin using the cointegration test used for determining the long-term relationship between Bitcoin price and volume.

This study aims to examine the volatility spillovers between Bitcoin (BTC), Litecoin (LTC) and Ethereum (ETH) as they are related to structural breaks.

This study examines the daily period from August 7, 2015 to July 10, 2018 by conducting causality-in-mean and causality-in-variance tests among cryptocurrencies.

The findings showed that there was one-way causality-in-mean from BTC to LTC and ETH, but there was no causality-in-mean from LTC and ETH to BTC. On the other hand, considering the structural breaks included in the variance equations, the estimation results showed that there were short-term causality-in-variance from LTC to BTC and long-term causality-in-variance from BTC to LTC.

This study fills the gap by contributing in two ways. First, to the best of the authors’ knowledge, this is the first study that used the cross-correlation function (CCF) of causality to explore causality-in-variance among cryptocurrencies. Second, this study considers the structural breaks in variance in the return series.