The motivation of this study was to analyze the volatility of Bitcoin, Ethereum, and Ripple cryptocurrencies in the global market. The weekly price and cryptocurrency trading datasets were outsourced from https.//Coinmarketcap.com. The period under study was from 1st February 2015 to 26th December 2021. Descriptive statistics for each cryptocurrency were analyzed and produced the following results. The mean for Ripple is 0.33, with a standard deviation of 0.39, a skewness of 1.97, and a kurtosis of 5.34 while the mean for Ethereum is 906.13, with a standard deviation of 1158.51, a skewness of 1.72 and kurtosis 1.8. The Mean for Bitcoin is 11242.34, standard deviation 15941.38, skewness 1.95, and kurtosis 2.67. This study was subjected to Garch Model analysis to determine the market volatility of Bitcoin, Ripple, and Ethereum cryptocurrencies. The analysis showed that ripple prices were constant from the years 2015 to 2017 low volatile then rose to high prices in the same year, the price variation with time was seen after 2017 to 2021, which means the prices were highly volatile. This suggested that the autocorrelation and seasonality of the structure of ripple cryptocurrency are not determinable. However, when data was subjected to compounding the return for ripple prices to check if there is any deviation in price variation through the study period, The result revealed that the highest volatility was presented in the year 2018. Ethereum price maintained a constant trend from 2018 to mid-2020 volatile and the prices increased with time to 2021 highly volatile as seen in figure 3. Bitcoin presented price variation with time as seen in figure 4, this shows a volatile market. By using Akaike Information Criterion was possible to identify the best Garch Models fitted to individual cryptocurrencies. This study has provided vital information to businesses, investors, and Governments to consider when making an informed decision regarding the type of cryptocurrencies to consider when making investment decisions, the price variability, and the volatility of cryptocurrencies in the market.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 11, Issue 6) |
| DOI | 10.11648/j.ajtas.20221106.15 |
| Page(s) | 219-224 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
Cryptocurrency, Volatility, Technology
| [1] | Fernández-Villaverde, J., Schilling, L., & Uhlig, H. (2020). Central bank digital currency: When price and bank stability collide. Available at SSRN 3753955. |
| [2] | Bordo, M. D., & Levin, A. T. (2017). Central bank digital currency and the future of monetary policy (No. w23711). National Bureau of Economic Research. |
| [3] | S. Nakamoto, ”Bitcoin: A Peer-to-peer Electronic Cash System.”2009. Retrieved from http://www.bitcoin.org/bitcoin.pdf. |
| [4] | Team, B. (2016, January 20). Understanding Bitcoin's Growth in 2015. Retrieved from Bitpay Website: https://blog.bitpay.com/understanding-bitcoins-growth-in-2015/ |
| [5] | Emaeyak Xavier Udom (2018), Estimating and Forecasting Bitcoin Daily Returns Using ARIMA-GARCH Models. |
| [6] | Raskin, M. (2012, November 29). Dollar-Less Iranians Discover Virtual Currency. Retrieved July 22, 2014, from Businessweek: http://www.businessweek.com/articles/2012-11-29/dollarless-iranians-discover-virtual-currency). |
| [7] | Hayek, F. A. (2013, March 3). Denationalization of Money. Retrieved September 4, 2014, from Institute of Economic Affairs. |
| [8] | Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3), 307-327. |
| [9] | Charlotte Christiansen, Maik Schmeling, and Andreas Schrimpf Monetary and Economic Department (2012). |
| [10] | Gil-Alana et al., 2020, Cryptocurrencies are susceptible to speculative bubbles since it is characterized by anonymity. Gil-Alana et al., in International Business and Finance, 51, 101063. https://doi.org/10.1016/j.ribaf.2019.101063 [Crossref], [Web of Science ®], [Google Scholar]). 2020 Gil-Alana, L. A., Abakah, E. J. A., & Rojo, M. F. R. (2020). |
| [11] | Yarovaya, L., Brzeszczyński, J., & Lau, C. K. M. (2016). Intra-and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures. International Review of Financial Analysis, 43, 96–114. https://doi.org/10.1016/j.irfa.2015.09.004 |
| [12] | Asafo-Adjei, E., Owusu Junior, P., & Adam, A. M. (2021). Information flow between Global Equities and Cryptocurrencies: A VMD-based entropy evaluating shocks from COVID-19 pandemic. Complexity, 2021. https://doi.org/10.1155/2021/4753753 |
| [13] | CoinMarketCap. 2017. Crypto-currency market capitalizations. Available online: https://coinmarketcap.com/ (accessed on 30 September 2017). |
APA Style
Douglas Wangila Khamila, Pius Kihara, Levi Mbugua. (2022). Analysis of Volatility of Cryptocurrencies in the Global Market. American Journal of Theoretical and Applied Statistics, 11(6), 219-224. https://doi.org/10.11648/j.ajtas.20221106.15
ACS Style
Douglas Wangila Khamila; Pius Kihara; Levi Mbugua. Analysis of Volatility of Cryptocurrencies in the Global Market. Am. J. Theor. Appl. Stat. 2022, 11(6), 219-224. doi: 10.11648/j.ajtas.20221106.15
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Download@article{10.11648/j.ajtas.20221106.15,
author = {Douglas Wangila Khamila and Pius Kihara and Levi Mbugua},
title = {Analysis of Volatility of Cryptocurrencies in the Global Market},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {11},
number = {6},
pages = {219-224},
doi = {10.11648/j.ajtas.20221106.15},
url = {https://doi.org/10.11648/j.ajtas.20221106.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20221106.15},
abstract = {The motivation of this study was to analyze the volatility of Bitcoin, Ethereum, and Ripple cryptocurrencies in the global market. The weekly price and cryptocurrency trading datasets were outsourced from https.//Coinmarketcap.com. The period under study was from 1st February 2015 to 26th December 2021. Descriptive statistics for each cryptocurrency were analyzed and produced the following results. The mean for Ripple is 0.33, with a standard deviation of 0.39, a skewness of 1.97, and a kurtosis of 5.34 while the mean for Ethereum is 906.13, with a standard deviation of 1158.51, a skewness of 1.72 and kurtosis 1.8. The Mean for Bitcoin is 11242.34, standard deviation 15941.38, skewness 1.95, and kurtosis 2.67. This study was subjected to Garch Model analysis to determine the market volatility of Bitcoin, Ripple, and Ethereum cryptocurrencies. The analysis showed that ripple prices were constant from the years 2015 to 2017 low volatile then rose to high prices in the same year, the price variation with time was seen after 2017 to 2021, which means the prices were highly volatile. This suggested that the autocorrelation and seasonality of the structure of ripple cryptocurrency are not determinable. However, when data was subjected to compounding the return for ripple prices to check if there is any deviation in price variation through the study period, The result revealed that the highest volatility was presented in the year 2018. Ethereum price maintained a constant trend from 2018 to mid-2020 volatile and the prices increased with time to 2021 highly volatile as seen in figure 3. Bitcoin presented price variation with time as seen in figure 4, this shows a volatile market. By using Akaike Information Criterion was possible to identify the best Garch Models fitted to individual cryptocurrencies. This study has provided vital information to businesses, investors, and Governments to consider when making an informed decision regarding the type of cryptocurrencies to consider when making investment decisions, the price variability, and the volatility of cryptocurrencies in the market.},
year = {2022}
}
TY - JOUR T1 - Analysis of Volatility of Cryptocurrencies in the Global Market AU - Douglas Wangila Khamila AU - Pius Kihara AU - Levi Mbugua Y1 - 2022/11/30 PY - 2022 N1 - https://doi.org/10.11648/j.ajtas.20221106.15 DO - 10.11648/j.ajtas.20221106.15 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 219 EP - 224 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20221106.15 AB - The motivation of this study was to analyze the volatility of Bitcoin, Ethereum, and Ripple cryptocurrencies in the global market. The weekly price and cryptocurrency trading datasets were outsourced from https.//Coinmarketcap.com. The period under study was from 1st February 2015 to 26th December 2021. Descriptive statistics for each cryptocurrency were analyzed and produced the following results. The mean for Ripple is 0.33, with a standard deviation of 0.39, a skewness of 1.97, and a kurtosis of 5.34 while the mean for Ethereum is 906.13, with a standard deviation of 1158.51, a skewness of 1.72 and kurtosis 1.8. The Mean for Bitcoin is 11242.34, standard deviation 15941.38, skewness 1.95, and kurtosis 2.67. This study was subjected to Garch Model analysis to determine the market volatility of Bitcoin, Ripple, and Ethereum cryptocurrencies. The analysis showed that ripple prices were constant from the years 2015 to 2017 low volatile then rose to high prices in the same year, the price variation with time was seen after 2017 to 2021, which means the prices were highly volatile. This suggested that the autocorrelation and seasonality of the structure of ripple cryptocurrency are not determinable. However, when data was subjected to compounding the return for ripple prices to check if there is any deviation in price variation through the study period, The result revealed that the highest volatility was presented in the year 2018. Ethereum price maintained a constant trend from 2018 to mid-2020 volatile and the prices increased with time to 2021 highly volatile as seen in figure 3. Bitcoin presented price variation with time as seen in figure 4, this shows a volatile market. By using Akaike Information Criterion was possible to identify the best Garch Models fitted to individual cryptocurrencies. This study has provided vital information to businesses, investors, and Governments to consider when making an informed decision regarding the type of cryptocurrencies to consider when making investment decisions, the price variability, and the volatility of cryptocurrencies in the market. VL - 11 IS - 6 ER -
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