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KRİPTO PARA POLİTİKASI BELİRSİZLİĞİ BİTCOİN FİYATININ BELİRLEYİCİSİ MİDİR?

Year 2022, Issue: 50, 369 - 378, 20.04.2022
https://doi.org/10.30794/pausbed.1027845

Abstract

Bu çalışma, “Kripto Para Birimi Politika Belirsizliği (UCRY Politikası) Bitcoin fiyatının (BTC) belirleyicisi midir?” sorusuna cevap bulmaya çalışmaktadır. Ayrıca, bu çalışma UCRY Politikasının yanı sıra Bitcoin'in dolaşımdaki hızı (BC), Bitcoin'in hesaplama gücü (HR), popülerlik (PO) ve döviz kuru gibi kontrol değişkenlerinin BTC hareketleri için açıklayıcı değişkenler olarak kullanmaktadır. Çalışmada, 30 Aralık 2013-21 Şubat 2021 dönem olarak belirlenmiş ve haftalık verilerle inceleme yapılmıştır. Değişkenler arasındaki ilişkiyi tespit etmek için ARDL sınır testi yöntemi kullanılmıştır. Ampirik bulgulara göre, bu çalışma UCRY Politikasının BTC için gerekli olduğunu göstermektedir. UCRY Politikası ile BTC arasında negatif bir ilişki vardır. Diğer değişkenler sabit tutulduğunda, UCRY Politikası arttığında BTC azalmaktadır. Ayrıca bu çalışma, kontrol değişkenlerinin BTC'nin belirleyicileri olarak kullanılabileceğini göstermektedir. Uzun vadede, BC ve HR, BTC ile önemli, pozitif bir ilişkisi vardır. EX'in BTC ile önemli, olumsuz bir ilişkisi vardır. PO'nun kısa vadede BTC ile önemli, pozitif bir ilişkisi vardır. Ayrıca bu çalışma, UCRY Politikasının Bitcoin için bir tür belirsizlik endeksi olarak kullanılabileceğini göstermektedir.

References

  • Akyildirim, E., Corbet, S., Lucey, B., Sensoy, A. and Yarovaya, L. (2020). “The Relationship between Implied Volatility and Cryptocurrency Returns”, Finance Research Letters, 33(101212), 1-10.
  • Alpago, H. (2018). “Bitcoin’den Selfcoin’e Kripto Para, Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 3(2), 411-428.
  • Baker, S. R., Bloom, N. and Davis J.S. (2016). “Measuring Economic Policy Uncertainty”, The quarterly journal of economics, 131(4), 1593–1636.
  • Balcilar, M., Bouri, E., Gupta, R. and Roubaud, D. (2017). “Can Volume Predict Bitcoin Returns and Volatility?”, A quantiles-based approach. Economic Modelling, 64, 74-81.
  • Bitcoin.com (2021). The velocity of Bitcoin in circulation. (10.09.2021) https://charts.bitcoin.com/bch/chart/velocity#5ma4.
  • Blockchain.com (2021). The estimated number of terahashes per second the Bitcoin network is performing in the last 24 hours. (10.09.2021) https://www.blockchain.com/charts/hash-rate.
  • Brianmlucey.wordpress.com (2021). The cryptocurrency uncertainty index data. (10.09.2021) https://brianmlucey.wordpress.com/2021/03/16/cryptocurrency-uncertainty-index-dataset/.
  • Bouoiyour, J. and Selmi, R. (2015). “What Does Bitcoin Look Like?”, Annals of Economics & Finance, 16(2), 449-492.
  • Chen, T., Lau, C. K. M., Cheema, S. and Koo, C. K. (2021). “Economic Policy Uncertainty in China and Bitcoin Returns: Evidence from the COVID-19 Period”, Frontiers in Public Health, 9(140), 1-7.
  • Ciaian, P. and Rajcaniova, M. (2016). “The Digital Agenda of Virtual Currencies: Can Bitcoin Become A Global Currency?”, Information Systems and e-Business Management, 14(4), 883-919.
  • Ciaian, P. and Rajcaniova, M. (2018). “Virtual Relationships: Short-and Long-Run Evidence from Bitcoin and Altcoin Markets”, Journal of International Financial Markets, Institutions and Money, 52, 173-195.
  • Coincodex (2021). Bitcoin Price. (08.09.2021) https://coincodex.com/crypto/bitcoin/?period=ALL
  • Colon, F., Kim, C., Kim, H. and Kim, W. (2021). “The Effect of Political and Economic Uncertainty on the Cryptocurrency Market”, Finance Research Letters, 39(101621), 1-7. Dickey, D. A. and Fuller, W. A. (1979). “Distribution of the Estimators for Autoregressive Time Series with A Unit Root”, Journal of the American statistical association, 74(366a), 427-431.
  • Engle, R. F. and Granger, C. W. (1987). “Co-Integration and Error Correction: Representation, Estimation, and Testing”, Econometrica: Journal of the Econometric Society, 55(2), 251-276.
  • Fang, T., Su, Z. and Yin, L. (2020). “Economic Fundamentals or Investor Perceptions? The Role of Uncertainty in Predicting Long-Term Cryptocurrency Volatility”, International Review of Financial Analysis, 71(101566), 1-12.
  • Garcia, D., Tessone, C. J., Mavrodiev, P. and Perony, N. (2014). “The Digital Traces of Bubbles: Feedback Cycles between Socio-Economic Signals in the Bitcoin Economy”, Journal of the Royal Society Interface, 11(99), 1-8.
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D. and Giaglis, G. M. (2015). “Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices”, available at: http://dx.doi.org/10.2139/ssrn.2607167 (accessed April 10, 2021).
  • Google Trends (2021). Bitcoin http://www.google.com/trends
  • Gozgor, G., Tiwari, A. K., Demir, E. and Akron, S. (2019). “The Relationship between Bitcoin Returns and Trade Policy Uncertainty”, Finance Research Letters, 29, 75-82. Hasan, M. B., Hassan, M. K., Karim, Z. A. and Rashid, M. M. (2021). “Exploring the Hedge and Safe Haven Properties of Cryptocurrency in Policy Uncertainty”, Finance Research Letters, 102272.
  • Hassan, M. K., Hasan, M. B. and Rashid, M. M. (2021). “Using Precious Metals to Hedge Cryptocurrency Policy and Price Uncertainty”, Economics Letters, 109977.
  • Hayes, A. (2015). “A Cost of Production Model for Bitcoin”, available at: http://dx.doi.org/10.2139/ssrn.2580904 (accessed 07.02, 2021).
  • Investing.com (2021a). Bitcoin’s price(BTC/USD). (11.09.2021) https:// www. investing.com /crypto /bitcoin /btc- usd-historical-data.
  • Investing.com (2021b). USD/EUR exchange rate. (11.09.2021) https://www.investing.com/currencies/usd-eur-historical-data.
  • Jang, H. and Lee, J. (2017). “An Empirical Study on Modeling and Prediction of Bitcoin Prices with Bayesian Neural Networks Based on Blockchain Information”, Ieee Access, 6, 5427-5437.
  • Johansen, S. (1988). “Statistical Analysis of Cointegration Vectors”, Journal of economic dynamics and control, 12(2-3), 231-254.
  • Johansen, S. and 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.
  • Kristoufek, L. (2013). “Bitcoin Meets Google Trends and Vikipedia: Quantifying The Relationship between Phenomena of the Internet Era”, Scientific reports, 3, 3415.
  • Kristoufek, L. (2015). “What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis”, PloS one, 10(4), 1-15.
  • Kwiatkowski, D., Phillips, P. C., Schmidt, P. and 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.
  • Li, Z., Chen, L. and Dong, H. (2021). “What are Bitcoin Market Reactions to Its-Related Events?”, International Review of Economics & Finance, 73, 1-10.
  • Lucey, B. M., Vigne, S. A., Yarovaya, L. and Wang, Y. (2021). “The Cryptocurrency Uncertainty Index”, Finance Research Letters, 102147, 1-14.
  • Nguyen, T., De Bodisco, C. and Thaver, R. (2018). “Factors Affecting Bitcoin Price in the Cryptocurrency Market: An Empirical Study”, International Journal of Business & Economics Perspectives, 13(1), 106-125.
  • Panagiotidis, T., Stengos, T. and Vravosinos, O. (2018). “On the Determinants of Bitcoin Returns: A LASSO Approach”, Finance Research Letters, 27, 235-240.
  • Pesaran, M. H. and Shin, Y. (1998). “An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis”, Econometric Society Monographs, 31, 371–413.
  • Pesaran, M. H., Shin, Y. and Smith, R. J. (2001). “Bounds Testing Approaches to the Analysis of Level Relationships”, Journal of Applied Econometrics, 16(3), 289-326.
  • Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski, R. and Lightfoot, G. (2015). “Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry”, International Journal of Electronic Commerce, 20(1), 9-49.
  • Poyser, O. (2017). “Exploring the Determinants of Bitcoin's Price: An Application of Bayesian Structural Time Series”, arXiv preprint arXiv:1706.01437, 1-47.
  • Shaikh, I. (2020). “Policy Uncertainty and Bitcoin Returns”, Borsa Istanbul Review, 20(3), 257-268.
  • Sovbetov, Y. (2018). “Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero”, Journal of Economics and Financial Analysis, 2(2), 1-27.
  • Sukamulja, S. and Sikora, C. O. (2018). “The New Era of Financial Innovation: The Determinants of Bitcoin's Price”, Journal of Indonesian Economy & Business, 33(1), 46-64.
  • Woo, X. L., Boon, K. K., Chee, P. J., Ngee, Z. Y. and Wong, K. S. (2019). “The Determinants of Crypto Currency Price: The Case of Bitcoin. unpublished manuscript, UTAR.
  • Wu, W., Tiwari, A. K., Gozgor, G. and Leping, H. (2020). “Does Economic Policy Uncertainty Affect Cryptocurrency Markets? Evidence from Twitter-Based Uncertainty Measures”, available at: http://dx.doi.org/10.2139/ssrn.3662748 (18.06.2021).

IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?

Year 2022, Issue: 50, 369 - 378, 20.04.2022
https://doi.org/10.30794/pausbed.1027845

Abstract

This study attempts to answer the question: “Is the Cryptocurrency Policy Uncertainty (UCRY Policy) a determinant of the Bitcoin’s price (BTC)?”. Besides, this study uses these factors as explanatory variables for the BTC movements alongside the UCRY Policy and control variables such as the velocity of the Bitcoin in circulation (BC), the computational power of Bitcoin (HR), popularity (PO), and exchange rate (EX). In the study, December 30, 2013- February 21, 2021, was determined as the term and weekly data were investigated. The ARDL bounds testing method was used to determine the relationship between the variables. According to empirical findings, this study suggests that the UCRY Policy is essential to the BTC. There is a negative relationship between UCRY Policy and BTC. When UCRY Policy increases, BTC decreases, holding other variables constant. Besides, this study shows that control variables can be used as determinants of BTC. In long run, BC and HR have a significant, positive relationship with BTC. The EX has a significant, negative relationship with BTC. The PO has a significant, positive relationship with BTC in the short run. In addition, this study demonstrates that UCRY Policy can be used as a type of uncertainty index for Bitcoin.

References

  • Akyildirim, E., Corbet, S., Lucey, B., Sensoy, A. and Yarovaya, L. (2020). “The Relationship between Implied Volatility and Cryptocurrency Returns”, Finance Research Letters, 33(101212), 1-10.
  • Alpago, H. (2018). “Bitcoin’den Selfcoin’e Kripto Para, Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 3(2), 411-428.
  • Baker, S. R., Bloom, N. and Davis J.S. (2016). “Measuring Economic Policy Uncertainty”, The quarterly journal of economics, 131(4), 1593–1636.
  • Balcilar, M., Bouri, E., Gupta, R. and Roubaud, D. (2017). “Can Volume Predict Bitcoin Returns and Volatility?”, A quantiles-based approach. Economic Modelling, 64, 74-81.
  • Bitcoin.com (2021). The velocity of Bitcoin in circulation. (10.09.2021) https://charts.bitcoin.com/bch/chart/velocity#5ma4.
  • Blockchain.com (2021). The estimated number of terahashes per second the Bitcoin network is performing in the last 24 hours. (10.09.2021) https://www.blockchain.com/charts/hash-rate.
  • Brianmlucey.wordpress.com (2021). The cryptocurrency uncertainty index data. (10.09.2021) https://brianmlucey.wordpress.com/2021/03/16/cryptocurrency-uncertainty-index-dataset/.
  • Bouoiyour, J. and Selmi, R. (2015). “What Does Bitcoin Look Like?”, Annals of Economics & Finance, 16(2), 449-492.
  • Chen, T., Lau, C. K. M., Cheema, S. and Koo, C. K. (2021). “Economic Policy Uncertainty in China and Bitcoin Returns: Evidence from the COVID-19 Period”, Frontiers in Public Health, 9(140), 1-7.
  • Ciaian, P. and Rajcaniova, M. (2016). “The Digital Agenda of Virtual Currencies: Can Bitcoin Become A Global Currency?”, Information Systems and e-Business Management, 14(4), 883-919.
  • Ciaian, P. and Rajcaniova, M. (2018). “Virtual Relationships: Short-and Long-Run Evidence from Bitcoin and Altcoin Markets”, Journal of International Financial Markets, Institutions and Money, 52, 173-195.
  • Coincodex (2021). Bitcoin Price. (08.09.2021) https://coincodex.com/crypto/bitcoin/?period=ALL
  • Colon, F., Kim, C., Kim, H. and Kim, W. (2021). “The Effect of Political and Economic Uncertainty on the Cryptocurrency Market”, Finance Research Letters, 39(101621), 1-7. Dickey, D. A. and Fuller, W. A. (1979). “Distribution of the Estimators for Autoregressive Time Series with A Unit Root”, Journal of the American statistical association, 74(366a), 427-431.
  • Engle, R. F. and Granger, C. W. (1987). “Co-Integration and Error Correction: Representation, Estimation, and Testing”, Econometrica: Journal of the Econometric Society, 55(2), 251-276.
  • Fang, T., Su, Z. and Yin, L. (2020). “Economic Fundamentals or Investor Perceptions? The Role of Uncertainty in Predicting Long-Term Cryptocurrency Volatility”, International Review of Financial Analysis, 71(101566), 1-12.
  • Garcia, D., Tessone, C. J., Mavrodiev, P. and Perony, N. (2014). “The Digital Traces of Bubbles: Feedback Cycles between Socio-Economic Signals in the Bitcoin Economy”, Journal of the Royal Society Interface, 11(99), 1-8.
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D. and Giaglis, G. M. (2015). “Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices”, available at: http://dx.doi.org/10.2139/ssrn.2607167 (accessed April 10, 2021).
  • Google Trends (2021). Bitcoin http://www.google.com/trends
  • Gozgor, G., Tiwari, A. K., Demir, E. and Akron, S. (2019). “The Relationship between Bitcoin Returns and Trade Policy Uncertainty”, Finance Research Letters, 29, 75-82. Hasan, M. B., Hassan, M. K., Karim, Z. A. and Rashid, M. M. (2021). “Exploring the Hedge and Safe Haven Properties of Cryptocurrency in Policy Uncertainty”, Finance Research Letters, 102272.
  • Hassan, M. K., Hasan, M. B. and Rashid, M. M. (2021). “Using Precious Metals to Hedge Cryptocurrency Policy and Price Uncertainty”, Economics Letters, 109977.
  • Hayes, A. (2015). “A Cost of Production Model for Bitcoin”, available at: http://dx.doi.org/10.2139/ssrn.2580904 (accessed 07.02, 2021).
  • Investing.com (2021a). Bitcoin’s price(BTC/USD). (11.09.2021) https:// www. investing.com /crypto /bitcoin /btc- usd-historical-data.
  • Investing.com (2021b). USD/EUR exchange rate. (11.09.2021) https://www.investing.com/currencies/usd-eur-historical-data.
  • Jang, H. and Lee, J. (2017). “An Empirical Study on Modeling and Prediction of Bitcoin Prices with Bayesian Neural Networks Based on Blockchain Information”, Ieee Access, 6, 5427-5437.
  • Johansen, S. (1988). “Statistical Analysis of Cointegration Vectors”, Journal of economic dynamics and control, 12(2-3), 231-254.
  • Johansen, S. and 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.
  • Kristoufek, L. (2013). “Bitcoin Meets Google Trends and Vikipedia: Quantifying The Relationship between Phenomena of the Internet Era”, Scientific reports, 3, 3415.
  • Kristoufek, L. (2015). “What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis”, PloS one, 10(4), 1-15.
  • Kwiatkowski, D., Phillips, P. C., Schmidt, P. and 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.
  • Li, Z., Chen, L. and Dong, H. (2021). “What are Bitcoin Market Reactions to Its-Related Events?”, International Review of Economics & Finance, 73, 1-10.
  • Lucey, B. M., Vigne, S. A., Yarovaya, L. and Wang, Y. (2021). “The Cryptocurrency Uncertainty Index”, Finance Research Letters, 102147, 1-14.
  • Nguyen, T., De Bodisco, C. and Thaver, R. (2018). “Factors Affecting Bitcoin Price in the Cryptocurrency Market: An Empirical Study”, International Journal of Business & Economics Perspectives, 13(1), 106-125.
  • Panagiotidis, T., Stengos, T. and Vravosinos, O. (2018). “On the Determinants of Bitcoin Returns: A LASSO Approach”, Finance Research Letters, 27, 235-240.
  • Pesaran, M. H. and Shin, Y. (1998). “An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis”, Econometric Society Monographs, 31, 371–413.
  • Pesaran, M. H., Shin, Y. and Smith, R. J. (2001). “Bounds Testing Approaches to the Analysis of Level Relationships”, Journal of Applied Econometrics, 16(3), 289-326.
  • Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski, R. and Lightfoot, G. (2015). “Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry”, International Journal of Electronic Commerce, 20(1), 9-49.
  • Poyser, O. (2017). “Exploring the Determinants of Bitcoin's Price: An Application of Bayesian Structural Time Series”, arXiv preprint arXiv:1706.01437, 1-47.
  • Shaikh, I. (2020). “Policy Uncertainty and Bitcoin Returns”, Borsa Istanbul Review, 20(3), 257-268.
  • Sovbetov, Y. (2018). “Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero”, Journal of Economics and Financial Analysis, 2(2), 1-27.
  • Sukamulja, S. and Sikora, C. O. (2018). “The New Era of Financial Innovation: The Determinants of Bitcoin's Price”, Journal of Indonesian Economy & Business, 33(1), 46-64.
  • Woo, X. L., Boon, K. K., Chee, P. J., Ngee, Z. Y. and Wong, K. S. (2019). “The Determinants of Crypto Currency Price: The Case of Bitcoin. unpublished manuscript, UTAR.
  • Wu, W., Tiwari, A. K., Gozgor, G. and Leping, H. (2020). “Does Economic Policy Uncertainty Affect Cryptocurrency Markets? Evidence from Twitter-Based Uncertainty Measures”, available at: http://dx.doi.org/10.2139/ssrn.3662748 (18.06.2021).
There are 42 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Articles
Authors

Yunus Karaömer 0000-0002-6377-1326

Early Pub Date May 15, 2022
Publication Date April 20, 2022
Acceptance Date February 8, 2022
Published in Issue Year 2022 Issue: 50

Cite

APA Karaömer, Y. (2022). IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(50), 369-378. https://doi.org/10.30794/pausbed.1027845
AMA Karaömer Y. IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?. PAUSBED. April 2022;(50):369-378. doi:10.30794/pausbed.1027845
Chicago Karaömer, Yunus. “IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, no. 50 (April 2022): 369-78. https://doi.org/10.30794/pausbed.1027845.
EndNote Karaömer Y (April 1, 2022) IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 50 369–378.
IEEE Y. Karaömer, “IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?”, PAUSBED, no. 50, pp. 369–378, April 2022, doi: 10.30794/pausbed.1027845.
ISNAD Karaömer, Yunus. “IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 50 (April 2022), 369-378. https://doi.org/10.30794/pausbed.1027845.
JAMA Karaömer Y. IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?. PAUSBED. 2022;:369–378.
MLA Karaömer, Yunus. “IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, no. 50, 2022, pp. 369-78, doi:10.30794/pausbed.1027845.
Vancouver Karaömer Y. IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?. PAUSBED. 2022(50):369-78.