Is bitcoin a near stock? Linear and non-linear causal evidence from a price–volume relationship

Abstract

Purpose The purpose of this paper is to examine the price–volume relationship in the bitcoin market to validate near-stock properties of bitcoin. Design/methodology/approach Daily data of bitcoin returns, returns volatility and trading volume (TV) are utilized for the period August 17, 2010–April 16, 2017. Linear and non-linear causality tests are employed to examine price–volume relationship in the bitcoin market. Findings The linear causality analysis indicates that the bitcoin TV cannot be used to predict return; however, the reverse causality is significant. In contrast, the non-linear causality analysis shows that there are non-linear feedbacks between the bitcoin TV and returns. The bitcoin TV, which represents new information, leads to price changes, and large positive price changes lead to increased trading activity. Similarly, in recent periods (post-break period), the results of the non-linear causality test show a unidirectional causality from TV to the volatility of returns. Research limitations/implications This study uses the average index value of major bitcoin exchanges. But further research on this relationship using data from different bitcoin exchanges may provide further insights into the price–volume relationship of bitcoin and its near-stock properties. Practical implications These findings from the non-linear causality analysis, therefore, suggest that investors cannot simply base their decisions on the linear dynamics of the bitcoin market. This is because new information in terms of the TV is neither linearly related to the price nor it is a one-to-one kind of relationship as most investors commonly understand it to be. Rather, investors’ decisions should be based on non-linear models, in general, and the best-fitting non-linear model, in particular. Originality/value The study examines bitcoin’s near-stock properties in a price–volume relationship framework with the help of both linear and non-linear causality tests, which to the best of the authors’ knowledge remains unexplored.

The relationship between trend and volume on the bitcoin market

“…The detected relationship is unidirectional, meaning that positive information from the cryptocurrency market encourages investors to make a stronger entrance into the market, which causes bubbles and further drives price increases. These results are in line with the findings based on the Toda-Yamamoto linear causality presented by, for example, Sahoo et al (2019) and Balcilar et al (2017), who also compared bullish and bearish markets and found no dependency between price and volume.…”

“…Relatively little attention was placed on investigating the volume-price relationship in the cryptocurrency market. For example, Sahoo et al (2019), based on the Granger causality developed by Toda and Yamamoto (1995) and the nonlinear Granger causality proposed by Diks and Panchenko (2005), received mixed results when analyzing the relationships between volume and price and volume and price variability. The application of linear Granger causality showed a significant causality between bitcoin returns and volume; hence, the authors found that price can be used to predict volume.…”

COVID-19 pandemic and cryptocurrency markets: an empirical analysis from a linear and nonlinear causal relationship

“…As a result, it helps the investors to determine their trading strategy. This causal relationship has extensively studied in the equity markets (Karpoff, 1987; Li et al , 2016), housing prices (Hui-Ching, 2014), currency future and interest rate (Kumar, 2017), market microstructure and noise trading (Chen and Lin, 2004; Silvapulle and Moosa, 1999) and Bitcoin price-volume prediction (Balcilar et al , 2017; Sahoo et al , 2019).…”

Multiscale Systemic Risk and Its Spillover Effects in the Cryptocurrency Market

“…e traditional nonlinear causality test does not have nonlinear predictive ability due to its low power [67,68]. erefore, this study 4 Complexity adopts the nonparametric test (D-P test) of nonlinear causality developed by Diks and Panchenko [69] in order to explore more causal relationships between related variables [70]. Since this test is nonparametric, it has advantages over parametric causality methods.…”