Testing Speculative Price Bubbles For Crypto Money Values : An Application For Bitcoin Abstract

Abstract

The aim of this study is to determination of the speculative price bubbles for crypto currencies by existence of bitcoin examining. The entity with the highest transaction volume within the crypto currencies is designated as Bitcoin, which is why it is thought that crypto money prices can reflect the data generation process. The values of all cryptocurrencies have recently shown a lot of price fluctuations. The speculative price bubbles can be shown as the cause of this change. If it is not the speculative price increase, it can be the result of the systematic risk increase of the market. The study period was attempted to select the period in which the volatility of Bitcoin asset returns increase. First, the standard Augmented Dickey-Fuller (ADF) test was used to test the stationary. In order to determine speculative price bubbles; GSADF (Phillips, Shi and Yu; 2013) test, which developed for the determination of multiple price bubbles in recursive way, takes into account the extreme right tail structure in the distributions. The purpose is to determine whether the cause of price change is speculative or not. 

Keywords

• Bitcoin,Crypto Money Values,Price Bubbles,ADF Unit Root Tests,Stationary

Kripto Para Değerleri için Spekülatif Fiyat Balonlarının Test Edilmesi : Bitcoin Üzerine Bir Uygulama

Abstract

Bu çalışmanın amacı; Bitcoin varlığının incelenerek, Kripto para birimleri için spekülatif fiyat şişkinliklerinin belirlenmesidir. Kripto para birimleri içinde işlem hacmi en yüksek olan varlık Bitcoin olarak belirlenmiş ve bu nedenle kripto para fiyatlarının veri üretme mekanizmasını yansıtabileceği düşünülmüştür. Tüm kripto para birimlerinin değerleri yakın zamanda çok dalgalanma göstermiştir. Bu değişimin nedeni olarak spekülatif fiyat şişkinlikleri gösterilebilir. Eğer neden spekülatif fiyat artışı değil ise piyasanın sistematik riskinin arttığı sonucuna varılabilir. Çalışma aralığı, Bitcoin varlığının getiri volatilitesinin arttığı dönem seçilmeye çalışılmıştır. Öncelikle durağanlığın test edilmesi amacıyla standart Arttırılmış Dickey-Fuller (ADF) testi kullanılmıştır. Fiyat şişkinliklerinin belirlenmesi için; dağılımlarında aşırı sağ kuyruk yapısını dikkate alan, özyinelemeli bir yapıya sahip olan ve çoklu fiyat balonlarının tespiti için geliştirilen GSADF (Phillips, Shi ve Yu; 2013) testi kullanılmıştır. Amaçlanan fiyat değişiminin nedeninin spekülatif olup olmadığının belirlenmesidir.

Keywords

• Bitcoin,Kripto Para Birimleri,Fiyat Balonları,ADF Birim Kök Testleri,Durağanlık

References

1. Wallace, B., (2011). “The Rise and Fall of Bitcoin.”, Wired Magazine, 19.12, http://www.wired.com/2011/11/mf_bitcoin/all/, Erişim Tarihi : 08.12..2018.
2. Grinberg, R., 2012. Bitcoin: an alternative digital currency. Hastings Sci. Technol. Law J. 159–208. Winter.
3. Plasaras, N., 2013. Regulating digital currencies: bringing Bitcoin within the reach of the IMF. Chic. J. Int. Law 14, 377.Maurer, B., Nelms, T.C., Swartz, L., 2013. ‘‘When perhaps the real problem is money itself!’’: the practical materiality of Bitcoin. Soc. Semiot. 23, 261–277.
4. Dowd, K., 2014. New Private Monies. A Bit-Part Player? Institute of Economic Affairs, London.
5. Garcia, D., Tessone, C.J., Mavrodiev, P., Perony, N., 2014. The digital traces of bubbles: feedback cycles between socio-economic signals in the bitcoin economy. J. R. Soc. Interface 11 (99), 20140623.
6. Kristoufek, L., 2015. What are the main drivers of the bitcoin price? evidence from wavelet coherence analysis. PLoS ONE 10 (4), e0123923.
7. Alabi, K., 2017. Digital blockchain networks appear to be following Metcalfe’s law. Electron. Commer. Res. Appl. 24, 23–29. http://dx.doi.org/10.1016/j.elerap.2017.06.003
8. Cheah, E.T., Fry, J., 2015. Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of bitcoin. Econ. Lett. 130, 32–36. http://dx.doi.org/10.1016/j.econlet.2015.02.029.

9. Fry, J., Cheah, E.T., 2016. Negative bubbles and shocks in cryptocurrency markets. Int. Rev. Financ. Anal. 47, 343–352. http://dx.doi.org/10.1016/j.irfa.2016.02.008.
10. Corbet, S., Larkin, C.J., Lucey, B.M., Meegan, A., Yarovaya, L., 2017. Cryptocurrency reaction to FOMC announcements: evidence of heterogeneity based on blockchain stack position. November 18. Available at SSRN: https://ssrn.com/abstract=3073727.
11. Corbet, S., Meegan, A., Larkin, C., Lucey, B.M., Yarovaya, L., 2017. Exploring the dynamic relationships between cryptocurrencies and other financial assets. November 13. Available at SSRN: https://ssrn.com/abstract=3070288.
12. Blau, B.M., 2017. Price dynamics and speculative trading in bitcoin. Res. Int. Bus. Finance 41, 493–499. http://dx.doi.org/10.1016/j.ribaf.2017.05.010.
13. Urquhart, A., 2017. Price clustering in bitcoin. Econ. Lett. 159, 145–148. http://dx.doi.org/10.1016/j.econlet.2017.07.035.
14. Phillips, P.C., Shi, S., Yu, J., 2015. Testing for multiple bubbles: historical episodes of exuberance and collapse in the S&P 500. Int. Econ. Rev. (Philadelphia) 56 (4), 1043–1078. http://dx.doi.org/10.1111/iere.12132.
15. Cheung, A.W.K., Roca, E., Su, J.J., 2015. Crypto-currency bubbles: an application of the Phillips, Shi and Yu (2013) methodology on mt. gox bitcoin prices. Appl. Econ. 47 (23), 2348–2358. http://dx.doi.org/10.1080/00036846.2015.1005827.
16. Phillips PCB, Shi S, Yu J (2013). “Testing for Multiple Bubbles 1: Historical Episodes of Exuberance and Collapse in the S&P 500.”.
17. David, A. Dickey ve Wayne Fuller (1979) Distribution of the Estimators for Autoregressive Time Series With a Unit Root Article in Journal of the American Statistical Association · June 1979 DOI: 10.2307/22863487.
18. Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root Author(s): David A. Dickey and Wayne A. Fuller Source: Econometrica, Vol. 49, No. 4 (Jul., 1981), pp. 1057-1072 Published by: The Econometric Society Stable URL: https://www.jstor.org/stable/1912517.
19. Hall, A. (1994), "Testing for a Unit Root in Time Series With Pretest Data- Based Model Selection," Journal of Business & Economic Statistics, 12, 461-470.
20. Phillips PCB, Wu Y, Yu J (2011). “Explosive Behavior in the 1990s NASDAQ: When Did Exuberance Escalate Asset Values?” International Economic Review, 52(1), 201–226.
21. Homm U, Breitung J (2012). “Testing for Speculative Bubbles in Stock Markets: A Comparison of Alternative Methods.” Journal of Financial Econometrics, 10(1), 198–231.
22. Bettendorf T, Chen W (2013). “Are there Bubbles in the Sterling-Dollar Exchange Rate? New Evidence From Sequential ADF Tests.” Economics Letters, 120, 350–353.
23. Evans GW (1991). “Pitfalls in Testing for Explosive Bubbles in Asset Prices.” The American Economic Review, 81(4), 922–930.
24. G¨urkaynak R (2008). “Econometric Tests of Asset Price Bubbles: Taking Stock.” Journal of Economic Surveys, 22(1), 166–186.
25. Phillips PCB, Yu J (2011). “Dating the Timeline of Financial Bubbles During the Subprime Crisis.” Quantitative Economics, 2(3), 455–491.
26. Caspi, I. (2013). Rtadf: Testing for Bubbles with EViews, https://mpra.ub.uni-muenchen.de/58791/1/MPRA_paper_58791.pdf.