Abstract
Bitcoin is a virtual currency scheme that is characterised by a decentralised network and cryptographic transfer verification. It has attracted much public attention due to its innovative technology and its high currency exchange rate volatility. In this paper, Bitcoin’s exchange rate movement from 2011 to 2018 and its relationship with the global financial markets are explored using an EGARCH framework. The results are as follows. First, fundamentals and Bitcoin-related specific events play a critical role in the formation of its exchange rate. Second, the impact on Bitcoin of regulation-related events indicates that market sentiment responds to market regulation statements. Third, news coverage is an essential factor in driving its volatility. Fourth, Bitcoin can be a hedge in times of low uncertainty in global financial markets and can also serve as a safe haven against high economic uncertainty worldwide, but with increasing global financial uncertainty, it is likely to move with the markets and therefore cannot serve as a hedge or safe haven against stock market crashes. Lastly, the positive effect of global expansionary monetary policy on Bitcoin’s exchange rate is marginal enough to rule out the involvement of central banks worldwide in the inflation of Bitcoin’s exchange rate over the years, as may have been the case with many asset prices after the 2008 US financial crisis.
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Notes
Bitcoin’s exchange rate on 29 November 2013 was USD 1242 per Bitcoin, while gold was traded at USD 1250 per ounce.
According to Baur and Lucey (2010), a hedge is an asset which is uncorrelated or negatively correlated with other assets on average, while a safe haven is an asset which shows these properties only in times of markets distress.
A number of papers dealing with investigating various economic indicators by using search volume index (SVI) from Google Trends have emerged over the past years (Da et al. 2011; Choi and Varian 2012; Preis et al. 2013; Scott and Varian 2015). Several papers, such as Bank et al. (2011), Aouadi et al. (2013), and Ding and Hou (2015), show that the SVI from Google Trends can serve as an adequate proxy for retail investor attention. In this sense, it would be interesting here to consider the impact of the SVI on Bitcoin’s exchange rate dynamics. However, when the observation range exceeds 90 days, the SVI is only available on a weekly basis, which means we cannot use its data because our study is based on daily data.
The Amihud (2002) measure is one of the most widely used liquidity proxies in the finance literature due to its simplicity and robustness.
For a Gaussian error distribution, the variance of the \(z_t\) is 1, and the conditional volatility corresponds to the time-varying scale parameter of the Gaussian distribution \(s_t\). For other error distributions, the conditional volatility of the time-varying scale parameters of the underlying distribution is multiplied by the unconditional volatility of the correspondingly distributed standardised residuals. See Ghalanos (2017) and Fernández and Steel (1998).
Markov-switching GARCH-type (MS-GARCH) models can be highly useful to capture the volatility clustering behaviour of the Bitcoin exchange rate, as shown by Ardia et al. (2018) and Caporale and Zekokh (2019). However, explanatory variables cannot be included in current MS-GARCH models, leaving them unsuitable for analysis in this paper.
The skewed GED distribution is a special case of the skewed generalised t (GT) distribution, which could be an interesting alternative to the former distribution. First implemented by Theodossiou (1998), the skewed GT distribution has two parameters that control the kurtosis, making it the most flexible distribution that takes into account the fat tail behaviour present in financial data. However, according to Ruiz and Nieto (2008), the implementation of this distribution proves to be difficult because the maximisation of the corresponding log-likelihood function is highly time-consuming.
Wang and Liu (2015) have shown that the average time lag between getting a Bitcoin from mining and selling it dropped from 19 days in 2011 to around 1.5 days in 2013. Accordingly, it is reasonable to assume that mining and selling now occur within 1 day and hence HR is included with no time lag.
Source: www.blockchain.com; author’s calculation.
The results are available upon request.
Tapering is the unwinding of the asset purchase volume regarding a quantitative easing programme.
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Acknowledgements
I am grateful for comments from Michael Funke. This research was supported by Section Z15 of the Deutsche Bundesbank. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the author and do not necessarily reflect the views of the Deutsche Bundesbank. My special thanks go to Marie Seifert for proofreading the first manuscript. Many thanks go to Maria Mohr for her mathematical advice. Last but not least, I would like to thank two anonymous reviewers for comments that greatly improved the paper.
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The author, Siwen Zhou, declares that he has no conflict of interest.
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Zhou, S. Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach. Empir Econ 60, 557–606 (2021). https://doi.org/10.1007/s00181-019-01776-4
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DOI: https://doi.org/10.1007/s00181-019-01776-4