Gox (one of the largest Bitcoin exchange markets). Meanwhile, to detect potential structural changes, we estimate our empirical model on two periods separated by the closure of Mt. To address co-integration in a mix of stationary and non-stationary time series, we use the autoregressive distributed lag (ARDL) model with a bounds test approach in the estimation. In this paper, we conduct a theory-driven empirical study of the Bitcoin exchange rate (against USD) determination, taking into consideration both technology and economic factors. Despite receiving extensive public attention, theoretical understanding is limited regarding the value of blockchain-based cryptocurrencies, as expressed in their exchange rates against traditional currencies. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of theĬryptocurrencies, such as Bitcoin, have ignited intense discussions. This result highlights the importance of modelling nonlinearity andĪccounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading The causality-in-quantiles test reveals that volume can predict returns – except inīitcoin bear and bull market regimes. Misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th DecemberĢ011 to 25th April 2016. The nonparametric characteristics of our test control for Whole of their respective conditional distributions. Test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the This study employs in contrast a non-parametric causality-inquantiles Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at theĬonditional mean of the returns distribution. Those additional analyses further support the findings and provide useful information for economic actors who are interested in adding Bitcoin to their equity portfolios or are curious about the capabilities of Bitcoin as a financial asset. Robustness analyses show, among others, a negative relation between the US implied volatility index (VIX) and Bitcoin volatility. They highlight the benefits of adding Bitcoin to a US equity portfolio, especially in the pre-crash period. As leverage effect and volatility feedback do not adequately explain this reaction, the authors propose the safe-haven effect (Baur, Asymmetric volatility in the gold market, 2012). This inverted asymmetric reaction of Bitcoin to positive and negative shocks is contrary to what one observes in equities. This finding shows that, prior to the price crash of December 2013, positive shocks increased the conditional volatility more than negative shocks. The authors test if there is a difference in the return-volatility relation before and after the price crash of 2013 and show a significant inverse relation between past shocks and volatility before the crash and no significant relation after. The results for the entire period provide no evidence of an asymmetric return-volatility relation in the Bitcoin market. The authors examine the relation between price returns and volatility changes in the Bitcoin market using a daily database denominated in US dollar.
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