Bitcoin and traditional currencies during the Covid-19 pandemic period

Chu, Meifen (2021): Bitcoin and traditional currencies during the Covid-19 pandemic period.

Abstract

The objective of this study is to examine the movement of Bitcoin and the traditional currencies (USD, EURO, GBP and CNY) and the Bitcoin’s hedging of the traditional currencies. First, this paper observes the Bitcoin and four traditional currency exchange series: the USD, EURO, GBP and CNY. Second, it examines the fluctuation patterns of each series by using wavelet transform analysis, Third, a wavelet coherence analysis is applied to examine the interdependence between the Bitcoin and the four traditional currencies. The phase pattern analysis results indicate that the Bitcoin may not act as a hedging currency to replace the traditional currencies during the Covid-19 crisis. Another interesting result shows the rapid increasing number of the World Covid-19 Deaths (CovidDeaths) may not be the critical reason for the hyper price of the Bitcoin. The massive quantitative easing (QE) may be considered as the key reason for the soar-up of the Bitcoin price.

• Item Type: MPRA Paper
• Language: English
• Keywords: Bitcoin, Traditional currencies, Covid-19, CovidDeaths, Hedging feature, Wavelet Analysis
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General

References

Aguiar-Conraria, L., Soares, M. J., (2011). Business cycle synchronization and the Euro: A wavelet analysis. Journal of Macroeconomics, 33(3), pp. 477-489.

Baur, D. G. and Dimpfl, T., (2020). The volatility of Bitcoin and its role as a medium of exchange and a store of value, Empirical Economics, https://doi.org/10.1007/s00181-020-01990-5

Böhme, R., Christin, N., Edelman, B., and Moore, T., 2015. Bitcoin: Economics, Techonology and Governance, Journal of Economic Perspectives, Vol. 29, No. 2, pp. 213-238.

Caporale, G. M., Kang, W., Spagnolo, F., and Spagnolo, N., 2021. Cyber-attacks, spillovers and contagion in the cryptocurrency markets, Journal of International Financial Markets, Institutions & Money, https://doi.org/10.1016/j.intfin.2021.101298

Caldara, D. and Iacoviello, M., 2019. Measuring Geopolitical Risk, Working Paper, December, Board of Governors of the Federal Reserve Board, Washington, DC. Google Scholar.

Chu, M., 2007. The analysis of the multifractal processes of stock prices in an artificial market with wavelet transform and its 'application to the analysis of agents' actions. The Annual Report of Economic Science, 45, pp. 121-127.

Demir, E., Bilgin, M. H., Karabulut, G., and Doker, A. C., 2020.The relationship between cryptocurrencies and COVID-19 pandemic. Eurasian Economic Review, 10(3), pp. 349-360.

Dyhrberg, A. H., (2016a), Bitcoin, gold and the dollar – A GARCH volatility analysis, Finance Research Letters, Vol.16, pp. 85–92.

Dyhrberg, A. H., (2016b), Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, Vol. 16, 139–144

Everson, R and Sirovich, L.,1990. Wavelet Analysis of the Turbulent Jet, Physics Letters A, Vol. 145, No. 6, 7, pp. 314 – 322.

García-Monleón, F., Danvila-del-Valle, I., Lara, F. J., 2021. Intrinsic value in crypto currencies, Technological Forecasting & Social Change, Vol. 162 (2021) 120393.

Grossmann, A. and Morlet, J., (1985). Decomposition of functions into wavelets of constant shape, and related transforms, Mathematics and physics - lectures on recent results, pp. 135-165.

Guo, X., and Donev, P., 2020. Bibliometrics and Network Analysis of Cryptocurrency Research, Journal of Systems Science and Complexity, vol. 33, pp.1933–1958.

Jeong, D. H., Kim, Y. D., Song, I. U., Chung, Y. A., and Jeong, J., 2016. Wavelet energy and wavelet coherence as EEG biomarkers for the diagnosis of Parkinson’s disease-related dementia and Alzheimer’s disease, Entropy, 18(1), pp. 1-17.

Jiang, S., Li, X., and Wang, S., 2021. Exploring evolution trends in cryptocurrency study: From underlying technology to economic applications, Finance Research Letters, Vol. 38 (2021) 101532.

Karabulut, G., Bilgin, M. H., and Doker, A. C., 2020. The relationship between commodity prices and world trade uncertainty. Economic Analysis and Policy, 66, pp. 276-281.

Kim, H. J., Hong, J. S., Hwang, H. C., Kim, S. M., and Han, D. H., 2020. Comparison of Psychological Status and Investment Style Between Bitcoin Investors and Share Investors, Frontiers in Psychology, 27 November 2020, https://doi.org/10.3389/fpsyg.2020.502295

Kleina, T., Thu, H., and Walthera, T., 2018. Bitcoin is not the New Gold – A comparison of volatility, correlation, and T portfolio performance, International Review of Financial Analysis, Vol. 59, pp. 105-116.

Kyriazis, N. A., 2020. The effects of geopolitical uncertainty on cryptocurrencies and other financial assets, SN Business & Economics, Vol. 1 (1), https://doi.org/10.1007/s43546-020-00007-8

Li, R., Li, S., Yuan, D., and Zhu, H., 2021. Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis, Research in International Business and Finance, Vol. 56 (2021) 101389.

Li, Z., Su, C., Qin, M., & Muhammad, U., (2020). Who is the chaser in cryptocurrencies? The Singapore Economic Review, https://doi.org/10.1142/S0217590820470049

Liu, X., Yu, X., Zhu, H., Yang, G., Wang Y., and Yu, X., 2020. A game‐theoretic approach of mixing different qualities of coins, International Journal of Intelligent Systems, https://doi.org/10.1002/int.22277

Mallat, S., 1998. A wavelet tour of signal processing. San Diego: Academic Press.

Mallat, S., 1999. A wavelet tour of signal processing. 2nd ed. San Diego: Academic Press.

Mallat, S., 2008. A wavelet tour of signal processing. 3rd ed. San Diego: Academic Press.

Mandelbrot, B., 1982. The fractal geometry of nature. San Francisco: Freeman.

Mandelbrot, B., 1999. A multifractal walk down Wall Street, Scientific American, 198(2), pp. 70-73.

Martinazzi, S., and Flori, A., 2020. The evolving topology of the Lightning Network: Centralization, efficiency, robustness, synchronization, and anonymity, Research Article, PLOS ONE, https://doi.org/10.1371/journal.pone.0225966

Morlet, J., Arens, G., Fourgeau, E., and Giard, D., 1982a. Wave propagation and sampling theory - Part I: Complex signal and scattering in multilayered media, Geophysics, 47, pp. 203-221.

Morlet, J., Arens, G., Fourgeau, E., and Giard, D., 1982b. Wave propagation and sampling theory – Part II: Sampling theory and complex waves, Geophysics, 47, pp. 222-236.

Nazifi, A., Murdy, S., Marder, B., Ga ̈thke, J., Shabani, B., 2021. A Bit(coin) of happiness after a failure: An empirical examination of the effectiveness of cryptocurrencies as an innovative recovery tool, Journal of Business Research, Vol. 124, pp. 494-505.

Nadarajah, S. and Chu, J., 2017. On the inefficiency of Bitcoin, Economics Letters, Vol. 150, pp. 6-9.

Manavi, S., A., Jafari, G., Rouhani, S., and Ausloos, M., 2020. Demythifying the belief in cryptocurrencies decentralized aspects. A study of cryptocurrencies time cross-correlations with common currencies, commodities and financial indices, Physica A, Vol. 556, 124759

Siu, T., and Elliott, R., 2020. Bitcoin option pricing with a SETAR-GARCH model, The European Journal of Finance, Vol. 27(6)

Torrence, C. and Compo, G. P., 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, Vol. 79(1), pp. p.61-78.

Urom, C., Abid, L., Guesmi, K., and Chevallier, J., 2020. Quantile spillovers and dependence between Bitcoin, equities and strategic commodities, Economic Modelling, Vol. 93, pp. 230-258.

Zhu, P., Zhang, X., Wu, Y., Zheng, H., and Zhang, Y., 2021. Investor attention and cryptocurrency: Evidence from the Bitcoin market, PLOS ONE, Published: February 1, 2021, https://doi.org/10.1371/journal.pone.0246331