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Year 2019, Volume: 6 Issue: 4, 315 - 319, 30.12.2019

İbrahim Mert

Abstract

References

  • Adrian (Wai-Kong) Cheung, Eduardo Roca & Jen-Je Su (2015) Cryptocurrency bubbles: an application of the Phillips–Shi–Yu (2013) methodology on Mt. Gox bitcoin prices, Applied Economics, 47:23, 2348-2358
  • Chiu, J., Koeppl, T.V. (2019). The Economics of Cryptocurrencies – Bitcoin and Beyond, Bank of Canada Staff Working Paper, 1-56.
  • Corbet, S., Lucey, B., Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum Bubbles, Finance Research Letters, 26, 81-88.
  • Dickey, D. and W. Fuller (1979). “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, 427-431.
  • Dickey, D. and W. Fuller (1981). “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica, 49, 1057-1072.
  • Geuder, J., Kinateder,H., Wagner, N.F. (2019). Cryptocurrencies as Financial Bubbles: The case of Bitcoin, Finance Research Letters, 31, 179 - 184.
  • Göçer, İ. (2016). Lisans ve lisansüstü için ekonometri. 1. Baskı. İzmir: Lider Yayınları.
  • Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models, Economic Letters, 158, 3-6.
  • Katsiampa, P., Corbet, S., Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis Finance Research Letters, 29, 68-74.
  • Koutmos, D. (2018). Bitcoin Returns and Transaction Activity, Economic Letters, 167, 81-85.
  • Lee Kuo Chuen, D. (ed.) Handbook of digital currency, 1st edn, Elsevier (2015)
  • Nakamoto, S., 2008. Bitcoin: A Peer-to-Peer Electronic Cash System. Bitocin.org.
  • Narayanan, A., Bonneau, J., Felten, E., Miller, A., Goldfeder, S. (2016). Bitcoin and Cryptocurrencies Technologies, Princeton University Press.
  • Mensi, W., Al-Yayhee, K.H., Kang, S.H. (2019), Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum, Finance Research Letters, 29, 222-230.
  • Ozyesil, M. (2019). A research on interaction between Bitcion and foreign exchange rates. Journal of Economics, Finance and Accounting (JEFA), V.6(1), p.55-62.
  • Perron, P. and S. Ng. (1996). “Useful Modifications to Some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties,” Review of Economic Studies, 63, 435-463.
  • Phillips, P.C.B. and P. Perron (1988). “Testing for Unit Roots in Time Series Regression,” Biometrika, 75, 335-346.
  • Tarı, R. (2012). Ekonometri. 8. Baskı. Kocaeli: Umuttepe Yayınları.
  • www.investing.com, (Accessed in 15.12.2019).

RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST

Year 2019, Volume: 6 Issue: 4, 315 - 319, 30.12.2019

İbrahim Mert

Abstract

Purpose - The major reason of performing this study is to examine volatility of the Bitcoin prices. As known, Bitcoin became more popular when its price movements changed radically. It has been increasing for years since the date of first issuance in 2010 and reached highest level in its history by testing 19,345 USD. Based on this price movement, risk and returns are taken together for making investment in Bitcoin since huge decreasing observed in respond to the these increases.
Methodology - In this study, Bitcoin prices are analyzed monthly basis through the time series analysis. Data related to closing prices of Bitcoin are obtained from investing.com web site. We established analysis based on sample consist of Bitcoin prices for the period between 2016 and 2019. Augmented Dickey Fuller (ADF) and Phillips Perron (PP) unit root test is applied to find out whether series are stationary or not.
Findings- According to test results, the series of Bitcoin prices are not stationary yet. Although different fluctuating degree can be seen by years, generally it can be stated that Bitcoin prices are still volatile.
Conclusion- Based on findings, Bitcoin prices may still be considered as volatile instrument. Therefore, investors should be careful when they want to include this investment tool to the portfolio since it represents risky instrument properties.

Keywords

Crypto currencies Bitcoin unit root test time series

References

  • Adrian (Wai-Kong) Cheung, Eduardo Roca & Jen-Je Su (2015) Cryptocurrency bubbles: an application of the Phillips–Shi–Yu (2013) methodology on Mt. Gox bitcoin prices, Applied Economics, 47:23, 2348-2358
  • Chiu, J., Koeppl, T.V. (2019). The Economics of Cryptocurrencies – Bitcoin and Beyond, Bank of Canada Staff Working Paper, 1-56.
  • Corbet, S., Lucey, B., Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum Bubbles, Finance Research Letters, 26, 81-88.
  • Dickey, D. and W. Fuller (1979). “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, 427-431.
  • Dickey, D. and W. Fuller (1981). “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica, 49, 1057-1072.
  • Geuder, J., Kinateder,H., Wagner, N.F. (2019). Cryptocurrencies as Financial Bubbles: The case of Bitcoin, Finance Research Letters, 31, 179 - 184.
  • Göçer, İ. (2016). Lisans ve lisansüstü için ekonometri. 1. Baskı. İzmir: Lider Yayınları.
  • Katsiampa, P. (2017). Volatility Estimation for Bitcoin: A Comparison of GARCH Models, Economic Letters, 158, 3-6.
  • Katsiampa, P., Corbet, S., Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis Finance Research Letters, 29, 68-74.
  • Koutmos, D. (2018). Bitcoin Returns and Transaction Activity, Economic Letters, 167, 81-85.
  • Lee Kuo Chuen, D. (ed.) Handbook of digital currency, 1st edn, Elsevier (2015)
  • Nakamoto, S., 2008. Bitcoin: A Peer-to-Peer Electronic Cash System. Bitocin.org.
  • Narayanan, A., Bonneau, J., Felten, E., Miller, A., Goldfeder, S. (2016). Bitcoin and Cryptocurrencies Technologies, Princeton University Press.
  • Mensi, W., Al-Yayhee, K.H., Kang, S.H. (2019), Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum, Finance Research Letters, 29, 222-230.
  • Ozyesil, M. (2019). A research on interaction between Bitcion and foreign exchange rates. Journal of Economics, Finance and Accounting (JEFA), V.6(1), p.55-62.
  • Perron, P. and S. Ng. (1996). “Useful Modifications to Some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties,” Review of Economic Studies, 63, 435-463.
  • Phillips, P.C.B. and P. Perron (1988). “Testing for Unit Roots in Time Series Regression,” Biometrika, 75, 335-346.
  • Tarı, R. (2012). Ekonometri. 8. Baskı. Kocaeli: Umuttepe Yayınları.
  • www.investing.com, (Accessed in 15.12.2019).
There are 19 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

İbrahim Mert This is me 0000-0003-3745-8405

Publication Date December 30, 2019
Published in Issue Year 2019 Volume: 6 Issue: 4

Cite

APA Mert, İ. (2019). RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST. Research Journal of Business and Management, 6(4), 315-319.
AMA Mert İ. RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST. RJBM. December 2019;6(4):315-319.
Chicago Mert, İbrahim. “RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST”. Research Journal of Business and Management 6, no. 4 (December 2019): 315-19.
EndNote Mert İ (December 1, 2019) RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST. Research Journal of Business and Management 6 4 315–319.
IEEE İ. Mert, “RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST”, RJBM, vol. 6, no. 4, pp. 315–319, 2019.
ISNAD Mert, İbrahim. “RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST”. Research Journal of Business and Management 6/4 (December 2019), 315-319.
JAMA Mert İ. RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST. RJBM. 2019;6:315–319.
MLA Mert, İbrahim. “RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST”. Research Journal of Business and Management, vol. 6, no. 4, 2019, pp. 315-9.
Vancouver Mert İ. RETURNS AND RISK ON BITCOIN: A VOLATILITY MEASUREMENT BY UNIT ROOT TEST. RJBM. 2019;6(4):315-9.

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