Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 10 Sayı: 1, 17 - 21, 30.12.2019

Öz

Kaynakça

  • Arltová,M., FEDOROVÁ, D.(2016). Selection of unit root test on the basis of length of the time series and value of AR (1) parameter. Statistika,96(3)
  • Bartos, J. (2015). Does bitcoin follow the hypothesis of efficient market? Interna- tional Journal of Economic Sciences, IV(2):10–23.
  • Bouoiyour, J. and Selmi, R. (2015). What does bitcoin look like? Annals of Economics and Finance, 16(2):449–492.
  • Bouoiyour, J., Selmi, R., Tiwari, A. K., & Olayeni, O. R. (2016). What drives Bitcoin price? Economics Bulletin, 36(2), 843–850.
  • 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.
  • Ciaian, P., Rajcaniova, M., and Kancs, D. (2016a). The digital agenda of virtual currencies: Can bitcoin become a global currency? Information Systems and e-Business Management, 14(4):883–919.
  • Ciaian, P., Rajcaniova, M., and Kancs, D. (2016b). The economics of bitcoin price formation. Applied Economics, 48(19):3–1815.
  • Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dy- namic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34.
  • Dikmen, Nedim (2012). Ekonomtri Temel Kavramlar ve Uygulamalar. 2. Baskı, Bursa: Dora Yayınları.
  • Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85–92.
  • FEDOROVÁ, D. Vybrané testy jednotkových kořenů v časových řadách. Diploma thesis, Prague: VŠE, 2016.
  • Gronwald, M. (2015). The economics of bitcoins - news, supply vs. demand and jumps. Discussion Paper in Economics, 17(15). University of Aberdeen Business School.
  • Huhtinen, T.-P. (2014). Bitcoin as a monetary system: Examining attention and at- tendance. Master’s thesis, Aalto University School of Business.
  • Li, X. and Wang, C. A. (2017). The technology and economic determinants of cryp- tocurrency exchange rates: The case of bitcoin. Decision Support Systems, 95:49 – 60. Kristoufek, L. (2013). Bitcoin meets google trends and wikipedia: Quantifying the relationship between phenomena of the internet era. Scientific Reports, 3.
  • Kristoufek, L. (2015). What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. Plos One, 10(4).
  • Ober, M., Katzenbeisser, S., & Hamacher, K. (2013). Structure and anonymity of the bitcoin transaction graph. Future Internet, 5(2), 237–250.
  • Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski, R., and Lightfoot, G. (2015). Price fluctuations and the use of bitcoin: An empirical inquiry. Inter- national Journal of Electronic Commerce, 20(1):9–49.
  • Rossi, E. (2011). Impulse response functions. http://economia.unipv.it/pagp/pagine_ personali /erossi/dottorato_svar.pdf, [Access Date: 02.03.2019].
  • Sims, C. A. (1980). Macroeconomics and reality. Econometrica. 48, 1-48.
  • Trenca, I., Mutu, S. (2011). Advantages and limitations of VAR models used in managing market risk in banks. Finance – Challenges of the Future, 13, 32-43.
  • Triacca, U. (2017). Vector autoregressive models. http://www.phdeconomics.sssup.it/ documents/Lesson17.pdf, [Access Date: 02.03.2019]
  • Yelowitz, A., & Wilson, M. (2015). Characteristics of Bitcoin users: An analysis of Google search data. Applied Economics Letters, 22(13), 1030–1036.

DETERMINANTS OF BITCOIN PRICES

Yıl 2019, Cilt: 10 Sayı: 1, 17 - 21, 30.12.2019

Öz

Purpose - The increase in the popularity of cryptocurrency market, various literature figure out the macroeconomic factors that effect the price movements of cryptocurrencies. This research aims to identify the interaction between gold, brent oil and bitcoin.
Methodology - The database includes the Daily prices of Bitcoin, gold and brent oil prices between the period of 28.04.2013-23.07.2019 which consist of 484 daily data. Natural logaritm for each indicator is used. First, the stationarity of the series were analyzed with ADF (Augmented Dickey Fuller) unit root test. Lag lengths are determined. Interactions between the series were analyzed by the Impulse-Response Function and Variance Decomposition methods.
Findings- The series are found out to be stationary at first difference. Impulse response graphs indicate that all variables respond in a reducing way to reducing shocks occurred in each indicator. Shocks have lost their effect on average in 5 days.
Conclusion- The results indicate that the effect of gold and brent oil prices on bitcoin daily prices do not have a strong effect. The results may be beneficial for investors to consider diversification for the portfolios.

Kaynakça

  • Arltová,M., FEDOROVÁ, D.(2016). Selection of unit root test on the basis of length of the time series and value of AR (1) parameter. Statistika,96(3)
  • Bartos, J. (2015). Does bitcoin follow the hypothesis of efficient market? Interna- tional Journal of Economic Sciences, IV(2):10–23.
  • Bouoiyour, J. and Selmi, R. (2015). What does bitcoin look like? Annals of Economics and Finance, 16(2):449–492.
  • Bouoiyour, J., Selmi, R., Tiwari, A. K., & Olayeni, O. R. (2016). What drives Bitcoin price? Economics Bulletin, 36(2), 843–850.
  • 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.
  • Ciaian, P., Rajcaniova, M., and Kancs, D. (2016a). The digital agenda of virtual currencies: Can bitcoin become a global currency? Information Systems and e-Business Management, 14(4):883–919.
  • Ciaian, P., Rajcaniova, M., and Kancs, D. (2016b). The economics of bitcoin price formation. Applied Economics, 48(19):3–1815.
  • Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dy- namic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34.
  • Dikmen, Nedim (2012). Ekonomtri Temel Kavramlar ve Uygulamalar. 2. Baskı, Bursa: Dora Yayınları.
  • Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85–92.
  • FEDOROVÁ, D. Vybrané testy jednotkových kořenů v časových řadách. Diploma thesis, Prague: VŠE, 2016.
  • Gronwald, M. (2015). The economics of bitcoins - news, supply vs. demand and jumps. Discussion Paper in Economics, 17(15). University of Aberdeen Business School.
  • Huhtinen, T.-P. (2014). Bitcoin as a monetary system: Examining attention and at- tendance. Master’s thesis, Aalto University School of Business.
  • Li, X. and Wang, C. A. (2017). The technology and economic determinants of cryp- tocurrency exchange rates: The case of bitcoin. Decision Support Systems, 95:49 – 60. Kristoufek, L. (2013). Bitcoin meets google trends and wikipedia: Quantifying the relationship between phenomena of the internet era. Scientific Reports, 3.
  • Kristoufek, L. (2015). What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. Plos One, 10(4).
  • Ober, M., Katzenbeisser, S., & Hamacher, K. (2013). Structure and anonymity of the bitcoin transaction graph. Future Internet, 5(2), 237–250.
  • Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski, R., and Lightfoot, G. (2015). Price fluctuations and the use of bitcoin: An empirical inquiry. Inter- national Journal of Electronic Commerce, 20(1):9–49.
  • Rossi, E. (2011). Impulse response functions. http://economia.unipv.it/pagp/pagine_ personali /erossi/dottorato_svar.pdf, [Access Date: 02.03.2019].
  • Sims, C. A. (1980). Macroeconomics and reality. Econometrica. 48, 1-48.
  • Trenca, I., Mutu, S. (2011). Advantages and limitations of VAR models used in managing market risk in banks. Finance – Challenges of the Future, 13, 32-43.
  • Triacca, U. (2017). Vector autoregressive models. http://www.phdeconomics.sssup.it/ documents/Lesson17.pdf, [Access Date: 02.03.2019]
  • Yelowitz, A., & Wilson, M. (2015). Characteristics of Bitcoin users: An analysis of Google search data. Applied Economics Letters, 22(13), 1030–1036.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Finans, İşletme
Bölüm Makaleler
Yazarlar

E.asena Deniz Bu kişi benim 0000-0003-1772-9714

Dilek Teker

Yayımlanma Tarihi 30 Aralık 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 10 Sayı: 1

Kaynak Göster

APA Deniz, E., & Teker, D. (2019). DETERMINANTS OF BITCOIN PRICES. PressAcademia Procedia, 10(1), 17-21.
AMA Deniz E, Teker D. DETERMINANTS OF BITCOIN PRICES. PAP. Aralık 2019;10(1):17-21.
Chicago Deniz, E.asena, ve Dilek Teker. “DETERMINANTS OF BITCOIN PRICES”. PressAcademia Procedia 10, sy. 1 (Aralık 2019): 17-21.
EndNote Deniz E, Teker D (01 Aralık 2019) DETERMINANTS OF BITCOIN PRICES. PressAcademia Procedia 10 1 17–21.
IEEE E. Deniz ve D. Teker, “DETERMINANTS OF BITCOIN PRICES”, PAP, c. 10, sy. 1, ss. 17–21, 2019.
ISNAD Deniz, E.asena - Teker, Dilek. “DETERMINANTS OF BITCOIN PRICES”. PressAcademia Procedia 10/1 (Aralık 2019), 17-21.
JAMA Deniz E, Teker D. DETERMINANTS OF BITCOIN PRICES. PAP. 2019;10:17–21.
MLA Deniz, E.asena ve Dilek Teker. “DETERMINANTS OF BITCOIN PRICES”. PressAcademia Procedia, c. 10, sy. 1, 2019, ss. 17-21.
Vancouver Deniz E, Teker D. DETERMINANTS OF BITCOIN PRICES. PAP. 2019;10(1):17-21.

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