Ayşe METİN KARAKAŞ, Aslıhan DEMİR, Sinan ÇALIK

Bitcoin ve Diğer Kripto Para Göstergelerinin Bağımsızlığı: CD Vine Copula Yaklaşımı

Son yıllarda, kopulaların karışım modeli ile birleşimine olan ilgi giderek artmaktadır. Sonlu bir karışım modeline dahil edilen asma kopulaların kombinasyonu, bir verideki gizli yapıların yakalanmasına da yardımcı olur. Bu makale, Bitcoin ve diğer kripto para göstergeleri arasındaki ilişkiyi CD Vine Copula Approach yöntemiyle incelemeyi amaçlamaktadır. Çalışma da Bitcoin, Bitcoin Cash, Ethereum, Litecoin ve IOTA finansal göstergelerinin kapanış fiyatlarını kullanıyoruz. Sonuçlar, bitcoin ve önemli finansal göstergeler arasında güçlü bir bağımlılık olduğunu göstermektedir.Anahtar Kelimeler: Vine Copula, C Vine copula, D vine copula

Interdependence of Bitcoin and Other Crypto Money Indicators: CD Vine Copula Approach

In recent years, there has been a growing interest on the combination of copulas with mixture model. The combination of vine copulas incorporated into a finite mixture model is also helpful to capture secret structures in a data. This paper aims to examine the relationship between bitcoin and other crypto money indicators with the CD Vine Copula Approach method. In the study, we use closing prices of Bitcoin, Bitcoin Cash, Ethereum, Litecoin, and IOTA. The results show that there is a strong dependence between bitcoin and prominent financial indicators.Keywords: Vine Copula, C Vine copula, D vine copula.

PDF

___

[1] Nakamoto S. 2008. Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/en/bitcoin-paper (Erişim tarihi: 17.06.2020).
[2] Eyal I., Sirer E.G. 2014. Majority is not enough: Bitcoin mining is vulnerable. In International conference on financial cryptography and data security. Springer, Berlin, Heidelberg, 436-454.
[3] Grinberg R. 2012. Bitcoin: An innovative alternative digital currency. Hastings Sci. & Tech. LJ, 4: 159.
[4] Yermack D. 2015. Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency, 31-43.
[5] Ron D., Shamir A. 2013. Quantitative analysis of the full bitcoin transaction graph. In International Conference on Financial Cryptography and Data Security, Springer, Berlin, Heidelberg 6-24.
[6] O'Dwyer K.J., Malone D. 2014. Bitcoin mining and its energy footprint. 25th IET Irish Signals & Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communities Technologies (ISSC 2014/CIICT 2014).
[7] Garay J., Kiayias A., Leonardos N. 2015. The bitcoin backbone protocol: Analysis and applications. In Annual International Conference on the Theory and Applications of Cryptographic Techniques, Springer, Berlin, Heidelberg, 281-310.
[8] Karame G.O., Androulaki E., Capkun S. 2012. Double-spending fast payments in bitcoin. In Proceedings of the 2012 ACM conference on Computer and communications security, 906- 917), ACM.
[9] Reid F., Harrigan M. 2013. An analysis of anonymity in the bitcoin system. In Security and privacy in social networks, Springer, New York, NY, 197-223.
[10] Kroll J.A., Davey I.C., Felten E.W. 2013. The economics of Bitcoin mining, or Bitcoin in the presence of adversaries. In Proceedings of WEIS, 2013: 11.
[11] Bonneau J., Miller A., Clark J., Narayanan A., Kroll J.A., Felten E.W. 2015. Sok: Research perspectives and challenges for bitcoin and cryptocurrencies. In Security and Privacy (SP), 2015 IEEE Symposium on, IEEE, 104-121.
[12] Böhme R., Christin N., Edelman B., Moore T. 2015. Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29 (2): 213-238.
[13] Sklar M. 1959. Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris, 8: 229-231.
[14] Nelson R.B. 1999. An Introduction to Copulas. Vol. 39, Lectures Notes in Statistics.
[15] Embrechts P., McNeil A., Straumann D. 2002. Correlation and dependence in risk management: properties and pitfalls. Risk management: value at risk and beyond, 176223.
[16] Cherubini U., Luciano E., Vecchiato W. 2004. Copula methods in finance. John Wiley & Sons, 1-310.
[17] Mitchell H., McKenzie M.D. 2003. GARCH model selection criteria. Quantitative Finance, 3 (4): 262-284.
[18] Patton A.J. 2006. Modelling asymmetric exchange rate dependence. International economic review, 47 (2): 527-556.
[19] Bollerslev T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31 (3): 307-327.
[20] Bollerslev T. 2009. Glossary to ARCH (GARCH. In Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle.
[21] Brooks C., Burke S.P. 2003. Information criteria for GARCH model selection. The European journal of finance, 9 (6): 557-580.
[22] Du J., Lai K.K. 2017. Modeling Dependence between European Electricity Markets with Constant and Time-varying Copulas. Procedia computer science, 122: 94-101.
[23] Albulescu C.T., Aubin C., Goyeau D., Tiwari A.K. 2018. Extreme co-movements and dependencies among major international exchange rates: A copula approach. The Quarterly Review of Economics and Finance, 69: 56-69.