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

Year 2020, , 1527 - 1536, 25.12.2020
https://doi.org/10.17798/bitlisfen.770929

Abstract

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 IOT. The results show that there is a strong dependence between bitcoin and prominent financial indicators.

References

  • [1] Nakamoto, S. 2008. Bitcoin: A peer-to-peer electronic cash system.
  • [2] Eyal, I., Sirer, E. G. 2014. Majority is not enough: Bitcoin mining is vulnerable. In International conference on financial cryptography and data security (pp. 436-454). Springer, Berlin, Heidelberg.
  • [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 (pp. 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 (pp. 6-24). Springer, Berlin, Heidelberg.
  • [6] O'Dwyer, K. J., Malone, D. 2014. Bitcoin mining and its energy footprint.
  • [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 (pp. 281-310). Springer, Berlin, Heidelberg.
  • [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 (pp. 906-917). ACM.
  • [9] Reid, F., Harrigan, M. 2013. An analysis of anonymity in the bitcoin system. In Security and privacy in social networks (pp. 197-223). Springer, New York, NY.
  • [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 (Vol. 2013, p. 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 (pp. 104-121). IEEE.
  • [12] Böhme, R., Christin, N., Edelman, B., Moore, T. 2015. Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-38.
  • [13] Sklar, M. 1959. Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris, 8, 229-
  • [14] Nelson, D. B. 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
  • [15] Nelson, R. B. 1999. An Introduction to Copulas, Lectures Notes in Statistics Vol. 39.
  • [16] Embrechts, P., McNeil, A., Straumann, D. 2002. Correlation and dependence in risk management: properties and pitfalls. Risk management: value at risk and beyond, 176223.
  • [17] Cherubini, U., Luciano, E., Vecchiato, W. 2004. Copula methods in finance. John Wiley & Sons.
  • [18] Mitchell, H., McKenzie, M. D. 2003. GARCH model selection criteria. Quantitative Finance, 3(4), 262-284.
  • [19] Patton, A. J. 2006. Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556.
  • [20] Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • [21] Bollerslev, T. 2009. Glossary to ARCH (GARCH. In Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle.
  • [22] Brooks, C., Burke, S. P. 2003. Information criteria for GARCH model selection. The European journal of finance, 9(6), 557-580.
  • [23] Du, J., Lai, K. K. 2017. Modeling Dependence between European Electricity Markets with Constant and Time-varying Copulas. Procedia computer science, 122, 94-101.
  • [24] 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.
  • [25] Brechmann, E., Schepsmeier, U. 2013. Cdvine: Modeling dependence with c-and d-vine copulas in r. Journal of Statistical Software, 52(3), 1-27.
  • [26] Czado, C., Brechmann, E. C., Gruber, L. 2013. Selection of vine copulas. In Copulae in Mathematical and Quantitative Finance (pp. 17-37). Springer, Berlin, Heidelberg.
  • [27] Czado, C. 2010. Pair-copula constructions of multivariate copulas. In Copula theory and its applications (pp. 93-109). Springer, Berlin, Heidelberg.
  • [28] Czado, C., Kastenmeier, R., Brechmann, E. C., Min, A. 2012. A mixed copula model for insurance claims and claim sizes. Scandinavian Actuarial Journal, 2012(4), 278-305.
  • [29] Brechmann, E., Schepsmeier, U. 2013. Cdvine: Modeling dependence with c-and d-vine copulas in r. Journal of Statistical Software, 52(3), 1-27.

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

Year 2020, , 1527 - 1536, 25.12.2020
https://doi.org/10.17798/bitlisfen.770929

Abstract

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 IOT. The results show that there is a strong dependence between bitcoin and prominent financial indicators.

References

  • [1] Nakamoto, S. 2008. Bitcoin: A peer-to-peer electronic cash system.
  • [2] Eyal, I., Sirer, E. G. 2014. Majority is not enough: Bitcoin mining is vulnerable. In International conference on financial cryptography and data security (pp. 436-454). Springer, Berlin, Heidelberg.
  • [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 (pp. 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 (pp. 6-24). Springer, Berlin, Heidelberg.
  • [6] O'Dwyer, K. J., Malone, D. 2014. Bitcoin mining and its energy footprint.
  • [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 (pp. 281-310). Springer, Berlin, Heidelberg.
  • [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 (pp. 906-917). ACM.
  • [9] Reid, F., Harrigan, M. 2013. An analysis of anonymity in the bitcoin system. In Security and privacy in social networks (pp. 197-223). Springer, New York, NY.
  • [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 (Vol. 2013, p. 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 (pp. 104-121). IEEE.
  • [12] Böhme, R., Christin, N., Edelman, B., Moore, T. 2015. Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-38.
  • [13] Sklar, M. 1959. Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris, 8, 229-
  • [14] Nelson, D. B. 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
  • [15] Nelson, R. B. 1999. An Introduction to Copulas, Lectures Notes in Statistics Vol. 39.
  • [16] Embrechts, P., McNeil, A., Straumann, D. 2002. Correlation and dependence in risk management: properties and pitfalls. Risk management: value at risk and beyond, 176223.
  • [17] Cherubini, U., Luciano, E., Vecchiato, W. 2004. Copula methods in finance. John Wiley & Sons.
  • [18] Mitchell, H., McKenzie, M. D. 2003. GARCH model selection criteria. Quantitative Finance, 3(4), 262-284.
  • [19] Patton, A. J. 2006. Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556.
  • [20] Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • [21] Bollerslev, T. 2009. Glossary to ARCH (GARCH. In Volatility and Time Series Econometrics: Essays in Honour of Robert F. Engle.
  • [22] Brooks, C., Burke, S. P. 2003. Information criteria for GARCH model selection. The European journal of finance, 9(6), 557-580.
  • [23] Du, J., Lai, K. K. 2017. Modeling Dependence between European Electricity Markets with Constant and Time-varying Copulas. Procedia computer science, 122, 94-101.
  • [24] 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.
  • [25] Brechmann, E., Schepsmeier, U. 2013. Cdvine: Modeling dependence with c-and d-vine copulas in r. Journal of Statistical Software, 52(3), 1-27.
  • [26] Czado, C., Brechmann, E. C., Gruber, L. 2013. Selection of vine copulas. In Copulae in Mathematical and Quantitative Finance (pp. 17-37). Springer, Berlin, Heidelberg.
  • [27] Czado, C. 2010. Pair-copula constructions of multivariate copulas. In Copula theory and its applications (pp. 93-109). Springer, Berlin, Heidelberg.
  • [28] Czado, C., Kastenmeier, R., Brechmann, E. C., Min, A. 2012. A mixed copula model for insurance claims and claim sizes. Scandinavian Actuarial Journal, 2012(4), 278-305.
  • [29] Brechmann, E., Schepsmeier, U. 2013. Cdvine: Modeling dependence with c-and d-vine copulas in r. Journal of Statistical Software, 52(3), 1-27.
There are 29 citations in total.

Details

Primary Language English
Journal Section Araştırma Makalesi
Authors

Ayse Karakaş 0000-0003-3552-0105

Aslıhan Demir 0000-0003-4532-1564

Sinan Çalik 0000-0002-4258-1662

Publication Date December 25, 2020
Submission Date July 17, 2020
Acceptance Date November 19, 2020
Published in Issue Year 2020

Cite

IEEE A. Karakaş, A. Demir, and S. Çalik, “Interdependence of Bitcoin and Other Crypto Money Indicators: CD Vine Copula Approach”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 4, pp. 1527–1536, 2020, doi: 10.17798/bitlisfen.770929.



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