Investigating the effects of financial volatility spillover between digital currencies (application of multivariate GARCH approach)

Document Type : Original Article

Authors

1 Ph.D. Candidate in Health Economics, Tarbiat Modares University, Tehran, Iran.

2 Assistant Prof, Department of Economics, Razi University, Kermanshah, Iran.

3 MSc Economics, Tarbiat Modares University, Tehran, Iran.

Abstract

Virtual money is one of the emerging phenomena that can be considered as one of the results of the penetration and expansion of cyberspace in human life. Facilitating financial transactions without the presence of intermediaries such as banks and financial institutions can be considered as one of the goals of creating virtual money. The purpose of this study is to investigate the effects of volatility spillover from Bitcoin as the largest digital currency on other digital currencies. In this study, the variables were converted into Rial currency to reflect Rial fluctuations simultaneously. One component of this analysis is identifying the digital currencies that have been most affected by the price bubbles and the free fall of bitcoin prices. The findings of the present study show that Bitcoin has the highest fluctuations on Dogecoin and dash among digital currencies, respectively, and it receives overflow from other digital currencies that have high transaction value. According to the results of the present study, the bubbles in the digital currency market show that the market is irrational and due to the effects of the existing overflow, it may spread to domestic financial markets and cause a lot of fluctuations.

Keywords

References

  1. Abu Bakr, Mohammad, (2017), Comprehensive jurisprudential study of bitcoin digital currencies and blockchain, translated by Mohammad Azarnivar, Retrieved June 30, 2021, (in persian)
  2. Asadi, Amirreza, (2016), "International Virtual Currencies and its Application in the Iranian Economy", the Third International Conference on New Findings in Science and Technology. (in persian)
  3. Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151.
  4. Bekiros, S. D. (2014). Contagion, decoupling and the spillover effects of the US financial crisis: Evidence from the BRIC markets. International Review of Financial Analysis, 33, 58-69.
  5. Bisinelli, A., Rizzini, S., & Moratti, D (2018), Bitcoin and Virtual Currencies The point of view of a lawyer. Retrieved July30, 2021 from http://www.fatf-gafi.org.
  6. Bouri, E., Azzi, G., & Dyhrberg, A. H. (2016). On the return-volatility relationship in the Bitcoin market around the price crash of 2013. A Retrieved Accessed June 30, 2021, https://www.rand.org/pubs/research_reports/RR1607.html.
  7. Caferra, R., Tedeschi, G., & Morone, A. (2021). Bitcoin: Bubble that bursts or Gold that glitters? Economics Letters,vol 205, 109942.
  8. Canh, N. P., Wongchoti, U., Thanh, S. D., & Thong, N. T. (2019). Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model. Finance Research Letters, 29, 90-100.
  9. Chaum, D. (1983). Blind signatures for untraceable payments. In Advances in cryptology(pp. 199-203). Springer, Boston, MA.
  10. Cheikh, N. B., Zaied, Y. B., & Chevallier, J. (2020). Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models. Finance Research Letters, 35, 101293.
  11. Chohan, U. W. (2017). A history of bitcoin. Available at SSRN 3047875.Chuen, D. L. K(2015), “Handbook of digital currency: Bitcoin, innovation, financial instruments, and big data”, Academic Press.
  12. Conrad J., Gultekin M.N. and G. Kaul (1991). “Asymmetric Predictability of Conditional Variances”. The Review of Financial Studies, 4(4), pp. 597-622.
  13. Dai, L., Hu, H., Chen, F., & Zheng, J. (2014). Volatility transmission in the dry bulk newbuilding and secondhand markets: an empirical research. Transportation Letters, 6(2), 57-66.
  14. Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
  15. Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold?. Finance Research Letters, 16, 139-144.
  16. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
  17. Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric reviews, 5(1), 1-50.
  18. Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 122-150.
  19. European central bank (2012), virtual currency schemes. Retrieved July30, 2021 from http://www.fatf-gafi.org.
  20. FATF, (2014), “Virtual Currencies Key Defnitons and Potental AML/CFT Risks”, Retrieved July July30, 2021 from http://www.fatf-gafi.org/, p4
  21. Fallah, R., kouchaki tajani, M., maranjory, M., Alikhanip, R. (2021). Presentation of a scenario-based optimization model for bank loan portfolio under conditions of uncertainty based on robust Mulvey's approach. Journal of Financial Management Perspective, 11(35). (in persian)
  22. Guo, J. & Chow, A. (2008), “Virtual Money Systems: a Phenomenal Analysis”, InECommerce Technology and the Fifth IEEE Conference on Enterprise Computing, ECommerce and E Services, 2008 10th IEEE Conference on, 267-272. IEEE.
  23. Huang, Y., Su, W., & Li, X. (2010, November). Comparison of BEKK GARCH and DCC GARCH models: an empirical study. International Conference on Advanced Data Mining and Applications (pp. 99-110). Springer, Berlin, Heidelberg.
  24. Huynh, T. L. D., Nasir, M. A., Vo, X. V., & Nguyen, T. T. (2020). “Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet. The North American Journal of Economics and Finance, 54, 101277.
  25. Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6.
  26. Katsiampa, P. (2019). Volatility co-movement between Bitcoin and Ether. Finance Research Letters, 30, 221-227.
  27. Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68-74.
  28. Kim, W., Lee, J., & Kang, K. (2020). The effects of the introduction of Bitcoin futures on the volatility of Bitcoin returns. Finance Research Letters, 33, 101204.
  29. Koutmos, D. (2018). Return and volatility spillovers among cryptocurrencies. Economics Letters, 173, 122-127.
  30. Li, H., & Majerowska, E. (2008). Testing stock market linkages for Poland and Hungary: A multivariate GARCH approach. Research in International Business and finance, 22(3), 247-266.
  31. Mirzakhani, Reza, (2017). Bitcoin and the financial-jurisprudential nature of virtual money. Stock Exchange and Securities Organization. (in persian)
  32. Moratis, G. (2021). Quantifying the spillover effect in the cryptocurrency market. Finance Research Letters, 38, 101534.
  33. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
  34. Omane-Adjepong, M., & Alagidede, I. P. (2019). Multiresolution analysis and spillovers of major cryptocurrency markets. Research in International Business and Finance, 49, 191-206.
  35. O'Neal, Stephen (2018), State-Issued Digital Currencies: The Countries Which Adopted, Rejected or Researched the Concept. Retrieved June 30, 2021,https://www.cnbc.com/2019/01/29/crime-still-plague cryptocurrencies-as-1point7-billion-was-stolen-last-year-.html.
  36. Osoolian, M., SadeghiSharif, S., Sharifiana, V. (2022). The Effect of Investor Sentiment on the Formation of Bubbles in the Stock Market. Journal of Financial Management Perspective, 11(35). (in persian)
  37. Park, S., Jang, K., & Yang, J. S. (2021). Information flow between bitcoin and other financial assets. Physica A: Statistical Mechanics and its Applications, 566, 125604.
  38. Pegan A. (1984). Econometric Issues in The Analysis of Regressions with Generated Regressors, International Economic Review, No. 25, pp:221-247.
  39. Pichl, L., & Kaizoji, T. (2017). Volatility analysis of bitcoin. Quantitative Finance and Economics, 1(4), 474-485.
  40. Rajabi, A. (2017). Bitcoin: A New Tool in the Electronic Payment System, Parliamentary Research Center. (in persian)
  41. Souri, A. (2012). Econometrics with the application of Eviews8 & Stata12, Tehran: Farhangshani Publications. (in persian)
  42. Sancheti, V. (2019). What Is Tether? - Everything You Need To Know About Tether Cryptocurrency. Retrieved June 30, 2021, https://www.un.org/zh/documents/treaty/files/A-RES-54-109.shtml.
  43. Wagner, Andrew (2014). digital vs virtual currencies, Retrieved July July30, 2021 from http://www.fatf-gafi.org.
  44. Wang, J. N., Lee, Y. H., Liu, H. C., & Lee, M. C. (2021). The determinants of positive feedback trading behaviors in Bitcoin markets. Finance Research Letters, 102120.
  45. Xu, Q., Zhang, Y., & Zhang, Z. (2021). Tail-risk spillovers in cryptocurrency markets. Finance Research Letters, 38, 101453.
  46. Yousaf, I., & Ali, S. (2020). Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic. Financial Innovation, 6(1), 1-18.
Financial Management Perspective
Volume 11, Issue 35
November 2021
Pages 143-172

Files

Share

How to cite

Statistics