Behavioural antecedents of Bitcoin trading volume

Abstract

This paper aims to examine the behavioural determinants of Bitcoin trading volume within a cross-country framework of 14 world economies plus the Eurozone. We introduce a basic taxonomy of behavioural indicators, distinguishing between consumer confidence, economic policy uncertainty (EPU), and indicators of financial volatility. Our estimations reveal that the Bitcoin trading volume can be predicted more accurately by EPU than by any other class of indicators. Finally, we identify the COVID-19 shock as a catalyst for a psychologically-driven Bitcoin market and find evidence that Bitcoin was a macro hedging instrument in the pandemic. To obtain our results, we conducted a panel Granger causality test, employing the Least Squares Dummy Variables (LSDV) estimator. Contrary to previous research, we found that market fundamentals (industrial production and equity market volume) became significant drivers of Bitcoin trading during the pandemic. This conclusion was preserved when we used the LSDV corrected estimator, which is more suitable for panels with a smaller time dimension. Apart from the practical implications for traders, this paper provides researchers with detailed steps for applying Granger causality testing in panel data settings.

Impact of technological interdependencies on employment by BCFE method

“…Moving from this consideration, Kiviet (1995) proposes a corrected LSDV estimation procedure to calculate the bias of Fixed Effects estimator. This procedure outperforms other estimators for smaller T [7,6,38]. In fact, Škrabić Perić [38] shows that when panel dimensions consist of T larger than N (N = 10, T = 15 or 30), the LSDVc estimator has lower Root Mean Squared Error (RMSE) not only in comparison to LSDV but also to GMM estimators.…”

“…This procedure outperforms other estimators for smaller T [7,6,38]. In fact, Škrabić Perić [38] shows that when panel dimensions consist of T larger than N (N = 10, T = 15 or 30), the LSDVc estimator has lower Root Mean Squared Error (RMSE) not only in comparison to LSDV but also to GMM estimators. In the same context, Buddelmeyer, H. et al [6] suggest that LSDVc performs well, even if T is smaller than N, when the coefficient of the lagged variable is γ = 0, 4.…”

A Monte Carlo study on the size and power of panel unit root tests

“…Some examples are datasets related to EU, G-20, or G-8 countries, on yearly, quarterly or monthly frequencies for shorter time periods [8,7]. In addition, smaller dimensions may result from the subdivision of datasets into subsets or subperiods [25]. As large values of N have been extensively researched in the existing literature, the focus of this paper is on values of N ≤ 30.…”