JournalsKart submission@journalskart.com 8882640642, 011-43101010
Register Login
Peer-Reviewed Open Access Journal

DIAS Technology Review

The Institute has a unique distinction of publishing a bi-annual International journal DIAS Technology Review – The International Journal for Business and IT. The Editorial Board comprises of...

P-ISSN: 0972-9658 English Since 2004
Submit Manuscript Browse Archives
Current Issue

Vol. 19 No. 1 (2022)

Articles 37th Edition of DTR Apr 2022 – Sept 2022
DOI 10.65301/dias.2022.19.1.2

A Quanatitatve Investigation of the Relationship between the Prices and Trading Volume of Bitcoin and Other Cryptocurrencies

Authors
MR
Mr. Shubham Gaur
Assistant Professor, Delhi School of Professional Studies and Research,Delhi
103 Views
240 Downloads
Published 2022-09-30
Pages 29-51
Abstract

The price of Bitcoin has been a topic of much interest in recent years, as it has experienced dramatic swings in value. However, the relationship between the price of Bitcoin and other cryptocurrencies is less well-understood. This paper presents a quantitative investigation of the relationship between the price of Bitcoin and the prices of other major cryptocurrencies, such as Ethereum, Litecoin, and Stellar. The paper uses correlation and regression analysis to investigate the relationship between the prices and volume traded of these cryptocurrencies. The results of the analysis show that there is a high degree of correlation between the prices of Bitcoin and other major cryptocurrencies. These findings indicate that when one cryptocurrency experiences a jump in value, there is an increased likelihood of similar jumps occurring in other cryptocurrencies. Notably, this co-jumping phenomenon is closely tied to spikes in trading volume. This underscores the critical role of trading volume fluctuations in influencing the overall volatility of cryptocurrencies. These results align with earlier research emphasizing the significance of trading volume in understanding the dynamics of cryptocurrency market volatility. This suggests that the price of Bitcoin may be a leading indicator for the prices of other cryptocurrencies. The findings of this paper suggest that the price of Bitcoin is not a standalone asset, but rather is influenced by the prices of other cryptocurrencies. This has important implications for investors who are considering investing in Bitcoin or other cryptocurrencies. The paper concludes by discussing the limitations of the study and by suggesting directions for future research. 

Keywords
Bitcoin Cryptocurrency Correlation Regression Price Digital Assets Interdependence Price Movements Market Analysis Cross-Cryptocurrency Analysis Time-Series Data Blockchain Assets
References
  1. Ali R., Barrdear J., Clews R., & Southgate J. (2014), ‘The economics of digital currencies Bank of England Quarterly Bulletin’, Q3, 276-286.
  2. Antonopoulos A. M. (2014), ‘Mastering Bitcoin: Unlocking digital cryptocurrencies’, O'Reilly Media.
  3. Blundell-Wignall A., & Roulet C. (2019), ‘The Bitcoin question: Currency versus trust-less transfer technology’, Journal of Financial Stability, 36, 225-236.
  4. Bouri E., Gupta R., Lau C. K. M., & Roubaud D. (2019), ‘Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles’, The Quarterly Review of Economics and Finance, 74, 328-340.
  5. Bouri E., Molnár P., Azzi G., Roubaud D., & Hagfors L. I. (2017), ‘On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?’, Finance Research Letters, 20, 192-198.
  6. 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.
  7. Chu J., Chan S., Nadarajah S., & Osterrieder J. (2017), ‘Statistical analysis of the exchange rate of Bitcoin’, PLoS ONE, 12(1), e0169836.
  8. Ciaian P., Rajcaniova M., & Kancs D. A. (2016), ‘The economics of BitCoin price formation’, Applied Economics, 48(19), 1799-1815.
  9. Corbet S., Lucey B., & Yarovaya L. (2018), ‘Date stamping the Bitcoin and Ethereum bubbles’, Finance Research Letters, 26, 81-88.
  10. Dwyer G. P. (2015), ‘The economics of Bitcoin and similar private digital currencies’, Journal of Financial Stability, 17, 81-91.
  11. Dyhrberg A. H. (2016), ‘Bitcoin, gold and the US dollar—a replication and extension’, Finance Research Letters, 16, 85-92.
  12. Garcia D., & Schweitzer F. (2015), ‘Social signals and algorithmic trading of Bitcoin’, Royal Society Open Science, 2(9), 150288.
  13. Garcia D., Tessone C. J., & Mavrodiev P. (2014), ‘The digital traces of bubbles: Feedback cycles between socio-economic signals in the Bitcoin economy’, Journal of the Royal Society Interface, 11(99), 20140623.
  14. Granger C. W. J., Maasoumi E., & Racine J. S. (2004), ‘A dependence metric for possibly non-linear processes’, Journal of Time Series Analysis, 25(5), 649-669.
  15. Hu S., Zhang Z., Tao R., & Yu J. (2017), ‘A long memory model with stochastic volatility for cryptocurrency market’, International Journal of Computer Mathematics, 94(11), 2358-2369.
  16. Jian, Z.-Q., Yang Y.-H., & Zhou W.-X. (2017), ‘Detrended cross-correlation analysis for cryptocurrency markets’, Physica A: Statistical Mechanics and its Applications, 484, 207-216.
  17. Kajtazi A., Moro A., & Teneqexhi D. (2018), ‘The effectiveness of Bitcoin as a hedge against economic uncertainty’, Finance Research Letters, 26, 145-149.
  18. Katsiampa P. (2017), ‘Volatility estimation for Bitcoin: A comparison of GARCH models’, Economics Letters, 158, 3-6.
  19. Kristoufek L. (2015), ‘What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis’, PLoS ONE, 10(4), e0123923.
  20. Li W., Wang X., Wang L., & Rong Y. (2017), ‘A dynamic copula-based GARCH model for cryptocurrency market risk forecasting’, Journal of Computational Science, 22, 68-77.
  21. McCauley J. L., Bauer M., & Ziman J. M. (2014), ‘Heterogeneity in cryptocurrency markets’, The European Physical Journal B, 87(11), 1-7.
  22. Mehta N., & Narang R. K. (2018), ‘Bitcoin price analysis and forecasting with ARIMA, GARCH, and neural networks’, Journal of King Saud University-Computer and Information Sciences, 30(4), 431-437.
  23. Momtaz P. P., & ElBahrawy A. (2019), ‘Sentiment-induced bubbles in the Bitcoin market’, PLoS ONE, 14(3), e0213398.
  24. Müller G., & Kroll J. (2018), ‘Is Bitcoin the only cryptocurrency in the town?’, Economics Letters, 163, 58-61.
  25. Nakamoto S. (2008), ‘Bitcoin: A peer-to-peer electronic cash system’, Retrieved from https://bitcoin.org/bitcoin.pdf
  26. Stavroyiannis S., Babalos V., Koulis A., & Kyriazis D. (2019), ‘Bitcoin and gold: Price analysis and volatility estimation using the spillover GARCH model’, Research in International Business and Finance, 48, 97-103.
  27. Urquhart A. (2018), ‘The inefficiency of Bitcoin revisited: A dynamic approach’, Economics Letters, 167, 18-21.
  28. Wang G., Xie C., & Liu B. (2018), ‘Realized volatility forecasting of Bitcoin: An economic analysis’, Finance Research Letters, 26, 169-17.
  29. Yelowitz A., & Wilson M. (2015), ‘Characteristics of Bitcoin users: An analysis of Google search data’, Applied Economics Letters, 22(13), 1030-1036.
  30. Yermack D. (2015), ‘Is Bitcoin a real currency? An economic appraisal’, Handbook of Digital Currency, 31-43.
Download
Download Pdf
How to Cite
Mr. Shubham Gaur . (2022). A Quanatitatve Investigation of the Relationship between the Prices and Trading Volume of Bitcoin and Other Cryptocurrencies. DIAS Technology Review, 19(1), 29-51.
About This Journal
DIA
DIAS Technology Review
Published by Journalskart
P-ISSN 0972-9658
✓ Citation copied to clipboard