×

Time series analysis of relationships among crypto-asset exchange rates. (English) Zbl 1455.91280

Pichl, Lukáš (ed.) et al., Advanced studies of financial technologies and cryptocurrency markets. Springer: Singapore. 139-162 (2020).
Summary: There are several previous empirical analyses for bitcoin pricing; however, only a few pieces of research can be found in terms of the relationships among major crypto assets, such as ethereum, and ripple. Here, we apply a method proposed by Z. Nan and the last author [“Market efficiency of the bitcoin exchange rate: weak and semi-strong form tests with the spot, futures and forward foreign exchange rates”, Int. Rev. Finance Anal. 64, 273–281 (2019; doi:10.1016/j.irfa.2019.06.003)], which calculates an indirect exchange rate to consider the possibility of a cointegrating relationship between a crypto-asset exchange rate and a direct FX spot rate. We investigate market efficiency in crypto-asset exchange rates through the application of several kinds of unit root tests and the Johansen procedure. The results suggest that the weak form of market efficiency does not seem to hold for all pairs; however, one of the prerequisites for semi-strong form of market efficiency holds for several pairs. Additionally, we focus on the dynamic relationships by applying the impulse response function for a four-variable VECM. Remarkably, the bitcoin exchange rate can slightly affect the EUR/USD spot rate.
For the entire collection see [Zbl 1455.91009].

MSC:

91G99 Actuarial science and mathematical finance
62P05 Applications of statistics to actuarial sciences and financial mathematics
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Zivot, E.
[2] Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters,148,80-82.
[3] Tiwari, A. K., Jana, R. K., Das, D., & Roubaud, D. (2018). Informational efficiency of Bitcoin—An extension. Economics Letters,163,106-109.
[4] Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics,6(2), 461-464. · Zbl 0379.62005
[5] Said, S. E., & Dickey, D. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika,71(3), 599-607. · Zbl 0564.62075
[6] R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
[7] Osterwald-Lenum, M. (1992). A note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics. Oxford Bulletin of Economics and Statistics,54(3), 461-472.
[8] Nan, Z., & Kaizoji, T. (2019). Market efficiency of the Bitcoin exchange rate: weak and semi-strong form tests with the spot, futures and forward foreign exchange rates. International Review of Financial Analysis,64,273-281.
[9] Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters,150,6-9.
[10] MacKinnon, J. G., Haug, A. A., & Michelis, L. (1999). Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics,14(5), 563-577.
[11] MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics,11(6), 601-618.
[12] Lütkepohl, H., Saikkonen, P., & Trenkler, C. (2001). Maximum eigenvalue versus trace tests for the cointegrating rank of a VAR process. The Econometrics Journal,4(2), 287-310. · Zbl 0995.62077
[13] Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? Journal of econometrics,54(1-3), 159-178. · Zbl 0871.62100
[14] Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration-with applications to the demand for money. Oxford Bulletin of Economics and Statistics,52(2), 169-210.
[15] Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica,59(6), 1551-1580. · Zbl 0755.62087
[16] Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control,12(2), 231-254. · Zbl 0647.62102
[17] Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society,B41,190-195. · Zbl 0408.62076
[18] Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press. · Zbl 0831.62061
[19] Fama, E. F. (1991). Efficient capital markets: II. The Journal of Finance,46(5), 1575-1617.
[20] Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. The Journal of Finance,25(2), 383-417.
[21] Dickey, D., & Fuller, W. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association,74(366), 427-431. · Zbl 0413.62075
[22] Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters,26,81-88.
[23] Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters,130,32-36. · Zbl 1321.91089
[24] Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control,AC-19,716-723. · Zbl 0314.62039
[25] Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics,21(1), 243-247. · Zbl 0202.17301
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.