Research Article
BibTex RIS Cite
Year 2024, Volume: 24 Issue: 4, 675 - 686, 01.11.2024
https://doi.org/10.21121/eab.20240413

Abstract

Project Number

yok

References

  • Akkaya. M. ve Tuna. İ. (2023). G7 (Gelişmiş 7) Ülkeleri Hisse Senedi Borsaları ile Bitcoin Fiyatı Arasındaki İlişkinin Analizi. Gaziantep University Journal of Social Sciences. 22(3). 949-959.
  • Almansour, B. Y., Almansour, A., & Alshater, M. (2021). Performance of ARCH and GARCH models in forecasting cryptocurrency market volatility. Industrial Engineering & Management Systems, 20(2), 130-139. https://doi.org/10.7232/iems.2021.20.2.130
  • Altunöz. U. (2023). Kripto Paraların Volatilite Dinamiklerinin ve Spekülatif Balon Varlığının Analizi: Bitcoin. Ethereum ve Ripple Örneği. İstanbul İktisat Dergisi. 73(1). 615-643.
  • Aththanayake. A. M. R. N. & Nanayakkara. N. S. (2023. April). The Impact of Market Sentiment and Macro-Financial Factors on Cryptocurrency Prices. In Student Conference in Finance 2023. 26.
  • Bouoiyour. J. & Selmi. R. (2017). The Bitcoin price formation: Beyond the fundamental sources. arXiv preprint arXiv:1707.01284.
  • Ciaian. P.. Kancs. D. A. & Rajcaniova. M. (2018). The price of Bitcoin: GARCH evidence from high frequency data. arXiv preprint arXiv:1812.09452.
  • Corbet. S.. Lucey. B.. Urquhart. A. & Yarovaya. L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis. 62. 182-199.
  • de la Horra. L. P.. de la Fuente. G.. & Perote. J. (2019). The drivers of Bitcoin demand: A short and long-run analysis. International Review of Financial Analysis. 62. 21-34.
  • Diniz. R.. de Prince. D. & Maciel. L. (2022). Bubble detection in Bitcoin and Ethereum and its relationship with volatility regimes. Journal of Economic Studies. (ahead-of-print).
  • Geuder. J.. Kinateder. H. & Wagner. N. F. (2019). Cryptocurrencies as financial bubbles: The case of Bitcoin. Finance Research Letters. 31. 179-184.
  • Goczek. Ł. & Skliarov. I. (2019). What drives the Bitcoin price? A factor augmented error correction mechanism investigation. Applied Economics. 51(59). 6393-6410.
  • Gonzalez, M. D. L. O., Jareno, F., & Skinner, F. S. (2020). Nonlinear autoregressive distributed lag approach: An application on the connectedness between bitcoin returns and the other ten most relevant cryptocurrency returns. Mathematics, 8(5), 810.
  • Greenberg. A. (2011). Crypto Currency. Forbes. https://www.forbes.com.
  • Helmi, M. H., Çatık, A. N., & Akdeniz, C. (2023). The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model. Research in International Business and Finance, 65, 101968.
  • Höl, A. Ö. (2024). Long Memory in Clean Energy Exchange Traded Funds. Politická ekonomie, 478-500, Işıldak. M. S. (2022). Kripto para piyasasında spekülatif balonlar: Bitcoin’den kanıtlar. Business Economics and Management Research Journal. 5(3). 209-219.
  • Li. X. & Wang. C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems. 95. 49-60.
  • Li, Z. Z., Tao, R., Su, C. W., & Lobonţ, O. R. (2019). Does Bitcoin bubble burst? Quality & Quantity, 53, 91-105.
  • Li. Y.. Wang. Z.. Wang. H.. Wu. M. & Xie. L. (2021). Identifying price bubble periods in the bitcoin market-based on GSADF model. Quality & Quantity. 55(5). 1829-1844.
  • Meegan, A., McHugh, G., & Corbet, S. (2017). The influence of Central Bank monetary policy announcements on cryptocurrency return volatility. Investment Management and Financial Innovations, 14(4), 60-72. https://doi.org/10.21511/imfi.14(4).2017.07
  • Nakamoto. S. (2008). Bitcoin: A peer-to-peer electronic cash system.
  • Panagiotidis. T.. Stengos. T. & Vravosinos. O. (2018). On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters. 27. 235-240.
  • Pesaran. M. H.. Shin. Y.. & Smith. R. J. (2001). Bounds Testing Approaches to the Analysis ofLevel Relationship. Journal of Applied Econometrics. 16(3). 289-326.
  • Phillips, P. C., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International economic review, 56(4), 1043-1078.
  • Prabhune. N.. Mahajan. A.. Mittal. M. P. & Kumar. R. (2023). Investigating the Dynamics of Cryptocurrencies with Financial Markets: Evidence from an ARDL Approach. Global Business Review.
  • Shahzad. S. J. H.. Anas. M. & Bouri. E. (2022). Price explosiveness in cryptocurrencies and Elon Musk’s tweets. Finance Research Letters.
  • Smales. L. A. (2022). Investor attention in cryptocurrency markets. International Review of Financial Analysis. 79. 101972.
  • Treleaven. P.. Brown. R.G. &Yang. D. (2017). Blockchain Technology in Finance. Computer. 50(9). 14-17.
  • Vaddepalli. S. & Antoney. L. (2018). Are economic factors driving Bitcoin transactions? an analysis of select economies. Finance Research Letters. 163(12). 106-109.
  • Wang, C. (2021). Different GARCH model analysis on returns and volatility in bitcoin. Management School, Liverpool University, London City, United Kingdom,1(1), 37-59.
  • Wang. X.. Chen. X. & Zhao. P. (2020). The relationship between Bitcoin and stock market. International Journal of Operations Research and Information Systems. 11(2). 22-35.
  • Yermack. D. (2013). Is Bitcoin a Real Currency? An economic appraisal. NBER working paper no 19747.

Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation

Year 2024, Volume: 24 Issue: 4, 675 - 686, 01.11.2024
https://doi.org/10.21121/eab.20240413

Abstract

The Bitcoin, one of the most discussed event of recent times, is the first cryptocurrency to be released. Bitcoin has the biggest market capitalization in the cryptocurrencies, and it is a decentralized payment tool that cannot be controlled by any government institution. Cryptocurrencies operate on top of a decentralized network, recording and verifying transactions using a technology called Blockchain. Many bubble price formations occurred in financial markets throughout history. Cryptocurrency markets are prone to manipulation because they are not connected to any center. This study aims to to investigate the buble formations in Bitcoin (BTC) US Dollar price for the period of 11 December 2017 - 31 July 2023, and also to determine the main financial variables affecting Bitcoin prices. ARDL Bounds test proves cointegration between Bitcoin price and dependent variables. Error Correction Model determines positive connection between BTC and Nasdaq100 index. A daily change in Nasdaq100 index leads to a 0.58% increase in BTC price. Model captures also negative relationship between Volatility Index, USD Index and BTC price. No long-term relationship emerges in the results. In the long run, unique and different variables shape BTC price formation.

Ethical Statement

Çıkar çatışması bulunmamaktadır

Supporting Institution

yok

Project Number

yok

Thanks

Editör ve hakemlere verecekleri destek için teşekkür ederim

References

  • Akkaya. M. ve Tuna. İ. (2023). G7 (Gelişmiş 7) Ülkeleri Hisse Senedi Borsaları ile Bitcoin Fiyatı Arasındaki İlişkinin Analizi. Gaziantep University Journal of Social Sciences. 22(3). 949-959.
  • Almansour, B. Y., Almansour, A., & Alshater, M. (2021). Performance of ARCH and GARCH models in forecasting cryptocurrency market volatility. Industrial Engineering & Management Systems, 20(2), 130-139. https://doi.org/10.7232/iems.2021.20.2.130
  • Altunöz. U. (2023). Kripto Paraların Volatilite Dinamiklerinin ve Spekülatif Balon Varlığının Analizi: Bitcoin. Ethereum ve Ripple Örneği. İstanbul İktisat Dergisi. 73(1). 615-643.
  • Aththanayake. A. M. R. N. & Nanayakkara. N. S. (2023. April). The Impact of Market Sentiment and Macro-Financial Factors on Cryptocurrency Prices. In Student Conference in Finance 2023. 26.
  • Bouoiyour. J. & Selmi. R. (2017). The Bitcoin price formation: Beyond the fundamental sources. arXiv preprint arXiv:1707.01284.
  • Ciaian. P.. Kancs. D. A. & Rajcaniova. M. (2018). The price of Bitcoin: GARCH evidence from high frequency data. arXiv preprint arXiv:1812.09452.
  • Corbet. S.. Lucey. B.. Urquhart. A. & Yarovaya. L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis. 62. 182-199.
  • de la Horra. L. P.. de la Fuente. G.. & Perote. J. (2019). The drivers of Bitcoin demand: A short and long-run analysis. International Review of Financial Analysis. 62. 21-34.
  • Diniz. R.. de Prince. D. & Maciel. L. (2022). Bubble detection in Bitcoin and Ethereum and its relationship with volatility regimes. Journal of Economic Studies. (ahead-of-print).
  • Geuder. J.. Kinateder. H. & Wagner. N. F. (2019). Cryptocurrencies as financial bubbles: The case of Bitcoin. Finance Research Letters. 31. 179-184.
  • Goczek. Ł. & Skliarov. I. (2019). What drives the Bitcoin price? A factor augmented error correction mechanism investigation. Applied Economics. 51(59). 6393-6410.
  • Gonzalez, M. D. L. O., Jareno, F., & Skinner, F. S. (2020). Nonlinear autoregressive distributed lag approach: An application on the connectedness between bitcoin returns and the other ten most relevant cryptocurrency returns. Mathematics, 8(5), 810.
  • Greenberg. A. (2011). Crypto Currency. Forbes. https://www.forbes.com.
  • Helmi, M. H., Çatık, A. N., & Akdeniz, C. (2023). The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model. Research in International Business and Finance, 65, 101968.
  • Höl, A. Ö. (2024). Long Memory in Clean Energy Exchange Traded Funds. Politická ekonomie, 478-500, Işıldak. M. S. (2022). Kripto para piyasasında spekülatif balonlar: Bitcoin’den kanıtlar. Business Economics and Management Research Journal. 5(3). 209-219.
  • Li. X. & Wang. C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems. 95. 49-60.
  • Li, Z. Z., Tao, R., Su, C. W., & Lobonţ, O. R. (2019). Does Bitcoin bubble burst? Quality & Quantity, 53, 91-105.
  • Li. Y.. Wang. Z.. Wang. H.. Wu. M. & Xie. L. (2021). Identifying price bubble periods in the bitcoin market-based on GSADF model. Quality & Quantity. 55(5). 1829-1844.
  • Meegan, A., McHugh, G., & Corbet, S. (2017). The influence of Central Bank monetary policy announcements on cryptocurrency return volatility. Investment Management and Financial Innovations, 14(4), 60-72. https://doi.org/10.21511/imfi.14(4).2017.07
  • Nakamoto. S. (2008). Bitcoin: A peer-to-peer electronic cash system.
  • Panagiotidis. T.. Stengos. T. & Vravosinos. O. (2018). On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters. 27. 235-240.
  • Pesaran. M. H.. Shin. Y.. & Smith. R. J. (2001). Bounds Testing Approaches to the Analysis ofLevel Relationship. Journal of Applied Econometrics. 16(3). 289-326.
  • Phillips, P. C., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International economic review, 56(4), 1043-1078.
  • Prabhune. N.. Mahajan. A.. Mittal. M. P. & Kumar. R. (2023). Investigating the Dynamics of Cryptocurrencies with Financial Markets: Evidence from an ARDL Approach. Global Business Review.
  • Shahzad. S. J. H.. Anas. M. & Bouri. E. (2022). Price explosiveness in cryptocurrencies and Elon Musk’s tweets. Finance Research Letters.
  • Smales. L. A. (2022). Investor attention in cryptocurrency markets. International Review of Financial Analysis. 79. 101972.
  • Treleaven. P.. Brown. R.G. &Yang. D. (2017). Blockchain Technology in Finance. Computer. 50(9). 14-17.
  • Vaddepalli. S. & Antoney. L. (2018). Are economic factors driving Bitcoin transactions? an analysis of select economies. Finance Research Letters. 163(12). 106-109.
  • Wang, C. (2021). Different GARCH model analysis on returns and volatility in bitcoin. Management School, Liverpool University, London City, United Kingdom,1(1), 37-59.
  • Wang. X.. Chen. X. & Zhao. P. (2020). The relationship between Bitcoin and stock market. International Journal of Operations Research and Information Systems. 11(2). 22-35.
  • Yermack. D. (2013). Is Bitcoin a Real Currency? An economic appraisal. NBER working paper no 19747.
There are 31 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Research Article
Authors

Murat Akkaya 0000-0002-7071-8662

Project Number yok
Early Pub Date October 24, 2024
Publication Date November 1, 2024
Acceptance Date October 2, 2024
Published in Issue Year 2024 Volume: 24 Issue: 4

Cite

APA Akkaya, M. (2024). Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation. Ege Academic Review, 24(4), 675-686. https://doi.org/10.21121/eab.20240413
AMA Akkaya M. Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation. ear. November 2024;24(4):675-686. doi:10.21121/eab.20240413
Chicago Akkaya, Murat. “Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation”. Ege Academic Review 24, no. 4 (November 2024): 675-86. https://doi.org/10.21121/eab.20240413.
EndNote Akkaya M (November 1, 2024) Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation. Ege Academic Review 24 4 675–686.
IEEE M. Akkaya, “Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation”, ear, vol. 24, no. 4, pp. 675–686, 2024, doi: 10.21121/eab.20240413.
ISNAD Akkaya, Murat. “Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation”. Ege Academic Review 24/4 (November 2024), 675-686. https://doi.org/10.21121/eab.20240413.
JAMA Akkaya M. Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation. ear. 2024;24:675–686.
MLA Akkaya, Murat. “Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation”. Ege Academic Review, vol. 24, no. 4, 2024, pp. 675-86, doi:10.21121/eab.20240413.
Vancouver Akkaya M. Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation. ear. 2024;24(4):675-86.