Abstract
Early analysis of Bitcoin concluded that it did not meet the economic conditions to be classified as a currency. Since this analysis interest in bitcoin has increased substantially. We investigate whether the introduction of futures trading in bitcoin is able to resolve the issues that stopped bitcoin from being considered a currency. Our analysis shows that spot volatility has increased following the announcement of the futures contracts, the futures contracts are not an effective hedging instrument and that price discovery is driven by uninformed investors in the spot market. The conclusion that bitcoin is a speculative asset rather than a currency is not altered by the introduction of futures trading.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A website which collects Bitcoin data from multiple exchanges and combines it to form a weighted average.
- 2.
The ARMA(1,1) model has the following form: \(\Delta {P_{t}}=\alpha _{0}+\beta _{1}\Delta {P_{t-1}}+\beta _{2}\Psi _{t-1}+\Psi _{t}\), while the GARCH(1,1) model specification also considers \(\sigma ^{2}=\alpha _{1}+\gamma _{1}\Psi ^{2}_{t-1}+\gamma _{2}\sigma ^{2}_{t-1}\) where the conditional variance term (\(\sigma ^{2}\)) is the one-period ahead forecast variance based on past information and is a function of three terms: the mean; news about volatility from the previous period, measured as the lag of the squared residual from the mean equation (the ARCH term \(\gamma _{1}\Psi ^{2}_{t-1}\)); and last period’s forecast variance (the GARCH term \(\gamma _{2}\sigma ^{2}_{t-1}\)). This specification interprets this period’s variance as being formed by a weighted average of a long-term average (the constant), the forecast variance from the last period (the GARCH term), and information about volatility observed in the previous period (the ARCH term).
- 3.
\(\Omega =\begin{pmatrix} \sigma ^{2}_{1} &{} \rho \sigma _{1}\sigma _{2} \\ \rho \sigma _{1}\sigma _{2} &{} \sigma ^{2}_{2} \end{pmatrix}\) and its Cholesky factorisation, \(\Omega =MM'\).
References
Bohl, M.T., Salm, C.A., Schuppli, M.: Price discovery and investor structure in stock index futures. J. Futures Mark. 31(3), 282–306 (2011)
Böhme, R., Christin, N., Edelman, B., Moore, T.: Bitcoin: economics, technology, and governance. J. Econ. Persp. 29(2), 213–38 (2015)
Cabrera, J., Wang, T., Yang, J.: Do futures lead price discovery in electronic foreign exchange markets? J. Futures Mark. 29(2), 137–156 (2009)
Cheah, E.-T., Fry, J.: Speculative bubbles in bitcoin markets? an empirical investigation into the fundamental value of bitcoin. Econ. Lett. 130, 32–36 (2015)
Choudhry, T.: Short-run deviations and optimal hedge ratio: evidence from stock futures. J. Multinational Financ. Manage. 13(2), 171–192 (2003)
Figlewski, S.: Hedging performance and basis risk in stock index futures. J. Financ. 39(3), 657–669 (1984)
Gonzalo, J., Granger, C.: Estimation of common long-memory components in cointegrated systems. J. Bus. Econ. Stat. 13(1), 27–35 (1995)
Gulen, H., Mayhew, S.: Stock index futures trading and volatility in international equity markets. J. Futures Markets Futures Options Other Deriv. Prod. 20(7), 661–685 (2000)
Hasbrouck, J.: One security, many markets: determining the contributions to price discovery. J. Financ. 50(4), 1175–1199 (1995)
Hauptfleisch, M., Putniņš, T.J., Lucey, B.: Who sets the price of gold? London or New York. J. Futures Markets 36(6), 564–586 (2016)
Kroner, K.F., Sultan, J.: Time-varying distributions and dynamic hedging with foreign currency futures. J. Financ. Quant. Anal. 28(4), 535–551 (1993)
Lee, C.L., Stevenson, S., Lee, M.-L.: Futures trading, spot price volatility and market efficiency: evidence from european real estate securities futures. J. Real Estate Financ. Econ. 48(2), 299–322 (2014)
Lepage, Y.: A combination of wilcoxon’s and ansari-bradley’s statistics. Biometrika 58(1), 213–217 (1971)
Mood, A.M.: On the asymptotic efficiency of certain nonparametric two-sampletests. Ann. Math. Stat. 25, 514–522 (1954)
Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System (2008)
Park, T.H., Switzer, L.N.: Bivariate garch estimation of the optimal hedge ratios for stock index futures: a note. J. Futures Markets 15(1), 61–67 (1995)
Putniņš, T.J.: What do price discovery metrics really measure? J. Empirical Financ. 23, 68–83 (2013)
Rosenberg, J.V., Traub, L.G.: Price discovery in the foreign currency futures and spot market. J. Deriv. 17(2), 7–25 (2009)
Ross, G.J., et al.: Parametric and nonparametric sequential change detection in r: the cpm package. J. Stat. Softw. 66(3), 1–20 (2015)
Ross, G.J., Tasoulis, D.K., Adams, N.M.: Nonparametric monitoring of data streams for changes in location and scale. Technometrics 53(4), 379–389 (2011)
Yan, B., Zivot, E.: A structural analysis of price discovery measures. J. Financ. Markets 13(1), 1–19 (2010)
Yermack, D.: Is bitcoin a real currency? an economic appraisal. In: Handbook of Digital Currency, pp. 31–43. Elsevier (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Corbet, S., Lucey, B., Peat, M., Vigne, S. (2019). What Sort of Asset? Bitcoin Analysed. In: Mehandjiev, N., Saadouni, B. (eds) Enterprise Applications, Markets and Services in the Finance Industry. FinanceCom 2018. Lecture Notes in Business Information Processing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-19037-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-19037-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19036-1
Online ISBN: 978-3-030-19037-8
eBook Packages: Computer ScienceComputer Science (R0)