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Dynamic Volatility Connectedness among Cryptocurrencies: Evidence from Time-Frequency Connectedness Networks

Yıl 2023, Cilt: 23 Sayı: 1, 29 - 50, 28.03.2023
https://doi.org/10.18037/ausbd.1272534

Öz

This study examines the time-varying connectedness among the realized volatilities of seven major cryptocurrencies between January 2020 and May 2022. To this end, we implement the time and frequency connectedness time-varying parameter vector autoregression (TVP-VAR) approaches. Our findings propose that (i) the COVID-19 pandemic significantly affected the dynamic connectedness; (ii) the total connectedness index hits its apex around the official announcement of the pandemic; (iii) in line with previous studies Ethereum, Bitcoin, and Link are the largest propagators/recipients of shocks; (iv) the tightest volatility interdependencies are related to the short-run.

Kaynakça

  • Abuzayed, B., Bouri, E., Al-Fayoumi, N. and Jalkh, N. (2021). Systemic risk spillover across global and country stock markets during the COVID-19 pandemic. Economic Analysis and Policy, 71, 180-197. doi: 10.1016/j.eap.2021.04.010.
  • Adekoya, O. B. and Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898. DOI: 10.1016/j.resourpol.2020.101898
  • Aharon, D. Y., Umar, Z. and Vo, X. V. (2021). Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies. Financial Innovation, 7(1), 1-25. doi: 10.1186/s40854-021-00274-w
  • Antonakakis, N., Chatziantoniou, I. and Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84. doi: 10.3390/jrfm13040084
  • Aslanidis, N., Bariviera, A. F. and Perez-Laborda, A. (2021). Are cryptocurrencies becoming more interconnected?. Economics Letters, 199, 109725. doi: 10.1016/j.econlet.2021.109725
  • Barigozzi, M., Hallin, M., Soccorsi, S. and von Sachs, R. (2021). Time-varying general dynamic factor models and the measurement of financial connectedness. Journal of Econometrics, 222(1), 324-343. doi: 10.1016/j.jeconom.2020.07.004
  • Barunik, J. and Ellington, M. (2020). Dynamic networks in large financial and economic systems. arXiv preprint arXiv:2007.07842. Retrieved from: https://researchain.net/archives/pdf/Dynamic-Networks-In-Large-Financial-And-Economic-Systems-2257138
  • Baruník, J. and Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. doi: 10.1093/jjfinec/nby001.
  • Borgards, O., Czudaj, R. L. and Van Hoang, T. H. (2021). Price overreactions in the commodity futures market: An intraday analysis of the Covid-19 pandemic impact. Resources Policy, 71, 101966. doi: 10.1016/j.resourpol.2020.101966.
  • Bouri, E., Cepni, O., Gabauer, D. and Gupta, R. (2021). Return connectedness across asset classes around the COVID-19 outbreak. International Review of Financial Analysis, 73, 101646. doi: 10.1016/j.irfa.2020.101646.
  • Bouri, E., Gabauer, D., Gupta, R. and Tiwari, A. K. (2021). Volatility connectedness of major cryptocurrencies: The role of investor happiness. Journal of Behavioral and Experimental Finance, 30, 100463. doi: 10.1016/j.jbef.2021.100463.
  • Chatziantoniou, I., Gabauer, D. and Marfatia, H. A. (2021). Dynamic connectedness and spillovers across sectors: Evidence from the Indian stock market. Scottish Journal of Political Economy, 6(3), 283-300. doi: 10.1111/sjpe.12291.
  • Choi, S. Y. (2022). Volatility spillovers among Northeast Asia and the US: Evidence from the global financial crisis and the COVID-19 pandemic. Economic Analysis and Policy, 73, 179-193. doi: 10.1016/j.eap.2021.11.014.
  • Corbet, S., Lucey, B., Urquhart, A. and Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199. doi: 10.1016/j.irfa.2018.09.003.
  • Corbet, S., Hou, Y. G., Hu, Y., Larkin, C. and Oxley, L. (2020). Any port in a storm: Cryptocurrency safe-havens during the COVID-19 pandemic. Economics Letters, 194, 109377. doi: 10.1016/j.econlet.2020.109377.
  • da Gama Silva, P. V. J., Klotzle, M. C., Pinto, A. C. F. and Gomes, L. L. (2019). Herding behavior and contagion in the cryptocurrency market. Journal of Behavioral and Experimental Finance, 22, 41-50. doi: 10.1016/j.jbef.2019.01.006
  • Dahir, A. M., Mahat, F., Noordin, B. A. A. and Ab Razak, N. H. (2020). Dynamic connectedness between Bitcoin and equity market information across BRICS countries: Evidence from TVP-VAR connectedness approach. International Journal of Managerial Finance, 16(3), 357-371. doi: /IJMF-03-2019-0117.
  • Diebold, F. X. and Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. J. Econometrics, 182(1), 119–134. doi: 10.1016/j.jeconom.2014.04.012
  • Diebold, F. X., Liu, L. and Yilmaz, K. (2017). Commodity connectedness (National Bureau of Economic Research Working Paper Working Paper No. w23685). doi: 10.3386/w23685
  • Fasanya, I. O., Oliyide, J. A., Adekoya, O. B. and Agbatogun, T. (2021a). How does economic policy uncertainty connect with the dynamic spillovers between precious metals and bitcoin markets?. Resources Policy, 72, 102077. doi: 10.1016/j.resourpol.2021.102077
  • Fasanya, I. O., Oyewole, O., Adekoya, O. B. and Odei-Mensah, J. (2021b). Dynamic spillovers and connectedness between COVID-19 pandemic and global foreign exchange markets. Economic Research-Ekonomska Istraživanja, 34(1), 2059-2084. doi: 10.1080/1331677X.2020.1860796.
  • Foglia, M. and Dai, P. F. (2021). “Ubiquitous uncertainties”: spillovers across economic policy uncertainty and cryptocurrency uncertainty indices. Journal of Asian Business and Economic Studies, 29(1), 35-49. doi: 10.1108/JABES-05-2021-0051.
  • Garman, M. B. and Klass, M. J. (1980). On the estimation of security price volatilities from historical data. Journal of Business, 53(1), 67-78. Retrieved from: https://www.jstor.org/stable/2352358?casa_token= NkiejJR35tIAAAAA%3AnVt7pl_bGcuhYFixMFfMhtff8toVeAtHsv1-iyiuCNF9g6OxXbWqre7jnJRQE0ImKyMwbGKjCDLSZ0G2R8YwYQcMsv1VaEKGTXeLhnCsZldADlhzfut9sw#metadata_info_tab_contents
  • Geuder, J., Kinateder, H. and Wagner, N. F. (2019). Cryptocurrencies as financial bubbles: The case of Bitcoin. Finance Research Letters, 31, 179-184. doi: 10.1016/j.frl.2018.11.011.
  • Goodell, J. W. and Goutte, S. (2021). Diversifying equity with cryptocurrencies during COVID-19. International Review of Financial Analysis, 76, 101781. doi: 10.1016/j.irfa.2021.101781.
  • Gunay, S. (2021). Comparing COVID-19 with the GFC: A shockwave analysis of currency markets. Research in International Business and Finance, 56, 101377. doi: 10.1016/j.ribaf.2020.101377.
  • Guo, X., Lu, F. and Wei, Y. (2021). Capture the contagion network of bitcoin–Evidence from pre and mid COVID-19. Research in International Business and Finance, 58, 101484. doi:10.1016/j.ribaf.2021.101484.
  • Jiang, Y., Lie, J., Wang, J. and Mu, J. (2021). Revisiting the roles of cryptocurrencies in stock markets: A quantile coherency perspective. Economic Modelling, 95, 21-34. doi: 10.1016/j.econmod.2020.12.002.
  • Katsiampa, P., Corbet, S. and Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68-74. doi: 10.1016/j.frl.2019.03.009.
  • Koop, G., Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101-116. doi: 10.1016/j.euroecorev.2014.07.002.
  • Kumar, A. S. and Anandarao, S. (2019). Volatility spillover in crypto-currency markets: Some evidences from GARCH and wavelet analysis. Physica A: Statistical Mechanics and its Applications, 524, 448-458. doi: 10.1016/j.physa.2019.04.154.
  • Kumar, A., Iqbal, N., Mitra, S. K., Kristoufek, L. and Bouri, E. (2022). Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak. Journal of International Financial Markets, Institutions and Money, 77, 101523. doi: 10.1016/j.intfin.2022.101523.
  • Li, Z. and Meng, Q. (2022). Time and frequency connectedness and portfolio diversification between cryptocurrencies and renewable energy stock markets during COVID-19. The North American Journal of Economics and Finance, 59, 101565. doi: 10.1016/j.najef.2021.101565.
  • Mensi, W., Sensoy, A., Aslan, A. and Kang, S. H. (2019). High-frequency asymmetric volatility connectedness between Bitcoin and major precious metals markets. The North American Journal of Economics and Finance, 50, 101031. doi: 10.1016/j.najef.2019.101031.
  • Minoiu, C., Kang, C., Subrahmanian, V. S. and Berea, A. (2015). Does financial connectedness predict crises?. Quantitative Finance, 15(4), 607-624. doi: 10.1080/14697688.2014.968358
  • Moratis, G. (2021). Quantifying the spillover effect in the cryptocurrency market. Finance Research Letters, 38, 101534. doi: 10.1080/14697688.2014.968358.
  • Naeem, M. A., Qureshi, S., Rehman, M. U. and Balli, F. (2022). COVID-19 and cryptocurrency market: Evidence from quantile connectedness. Applied Economics, 54(3), 280-306. doi: 10.1080/00036846.2021.1950908
  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. [Online]. Available: https://bitcoin.org/bitcoin.pdf. [Accessed 22 March 2023].
  • Nasreen, S., Tiwari, A. K. and Yoon, S. M. (2021). Dynamic connectedness and portfolio diversification during the coronavirus disease 2019 pandemic: Evidence from the cryptocurrency market. Sustainability, 13(14), 7672. doi: 10.3390/su13147672.
  • Nguyen, K. Q. (2022). The correlation between the stock market and Bitcoin during COVID-19 and other uncertainty periods. Finance Research Letters, 46, 102284. doi: 10.1016/j.frl.2021.102284
  • Papathanasiou, S., Vasiliou, D., Magoutas, A. and Koutsokostas, D. (2022). Do hedge and merger arbitrage funds actually hedge? A time-varying volatility spillover approach. Finance Research Letters, 44, 102088. doi: 10.1016/j.frl.2021.102088.
  • Polat, O. and Günay, E. K. (2021). Cryptocurrency connectedness nexus the COVID-19 pandemic: evidence from time-frequency domains. Studies in Economics and Finance, 38(5), 946-963. doi: 10.1108/SEF-01-2021-0011.
  • Rai, K. and Garg, B. (2022). Dynamic correlations and volatility spillovers between stock price and exchange rate in BRIICS economies: Evidence from the COVID-19 outbreak period. Applied Economics Letters, 29(8), 738-745. doi: 10.1080/13504851.2021.1884835.
  • Sakurai, Y. and Kurosaki, T. (2020). How has the relationship between oil and the US stock market changed after the Covid-19 crisis?. Finance Research Letters, 37, 101773. doi: 10.1016/j.frl.2020.101773.
  • Samitas, A., Kampouris, E. and Polyzos, S. (2022). Covid-19 pandemic and spillover effects in stock markets: A fiancial network approach. International Review of Financial Analysis, 80, 102005. doi: 10.1016/j.irfa.2021.102005.
  • Shahzad, S. J. H., Naeem, M. A., Peng, Z. and Bouri, E. (2021). Asymmetric volatility spillover among Chinese sectors during COVID-19. International Review of Financial Analysis, 75, 101754. doi: 10.1016/j.irfa.2021.101754.
  • Su, X. and Li, Y. (2020). Dynamic sentiment spillovers among crude oil, gold, and Bitcoin markets: Evidence from time and frequency domain analyses. Plos one, 15(12), e0242515. doi: 10.1371/journal.pone.0242515.
  • Symitsi, E. and Chalvatzis, K. J. (2018). Return, volatility and shock spillovers of Bitcoin with energy and technology companies. Economics Letters, 170, 127-130. doi: 10.1016/j.econlet.2018.06.012.
  • Umar, Z., Adekoya, O. B., Oliyide, J. A. and Gubareva, M. (2021). Media sentiment and short stocks performance during a systemic crisis. International Review of Financial Analysis, 78, 101896. doi: 10.1016/j.irfa.2021.101896.
  • Wang, D., Li, P. and Huang, L. (2022a). Time-frequency volatility spillovers between major international financial markets during the COVID-19 pandemic. Finance Research Letters, 46, 102244. doi: 10.1016/j.frl.2021.102244.
  • Wang, Q., Wei, Y., Wang, Y. and Liu, Y. (2022b). On the Safe-Haven Ability of Bitcoin, Gold, and Commodities for International Stock Markets: Evidence from Spillover Index Analysis. Discrete Dynamics in Nature and Society, Special Issue: Fintech and Financial Risk Analysis in the Era of Big Data 2021, 1-16. doi: 10.1155/2022/9520486
  • Yi, S., Xu, Z. and Wang, G. J. (2018). Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?. International Review of Financial Analysis, 60, 98-114. doi: 10.1016/j.irfa.2018.08.012.
  • Zhang, H., Hong, H., Guo, Y. and Yang, C. (2022). Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains. International Review of Economics and Finance, 78, 267-285. doi: 10.1016/j.iref.2021.12.005
  • Zheng, Z., Xie, S., Dai, H., Chen, X. and Wang, H. (2017, June). An overview of blockchain technology: Architecture, consensus, and future trends. In 2017 IEEE international congress on big data (BigData congress) (pp. 557-564). Ieee. doi: 10.1109/BigDataCongress.2017.85
Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Onur Polat Bu kişi benim

Yayımlanma Tarihi 28 Mart 2023
Gönderilme Tarihi 2 Mart 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 23 Sayı: 1

Kaynak Göster

APA Polat, O. (2023). Dynamic Volatility Connectedness among Cryptocurrencies: Evidence from Time-Frequency Connectedness Networks. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 23(1), 29-50. https://doi.org/10.18037/ausbd.1272534