Cryptocurrency Interdependencies and COVID-19: The Diebold-Yilmaz and the Frequency Connectedness Approaches

Yıl 2022, , 283 - 300, 31.01.2022

Onur Polat , Gözde Eş Polat

https://doi.org/10.17233/sosyoekonomi.2022.01.14

Cited By: 1

Öz

It is well-known that financial connectedness tends to surge during financial/geopolitical turmoils. To this end, this study examines the impact of the COVID-19 pandemic on cryptocurrency connectedness by employing the Diebold-Yilmaz and the frequency connectedness approaches. Total spillover indexes estimated by both methodologies create proper signs to the 2017/2018 cryptocurrency bubble and gradually escalate around March 2020, which coincides with the WHO's official announcement of the COVID-19. The study contributes to the literature by gauging the COVID-19 connectedness among eight major cryptocurrencies on different frequency bands and 200-day moving windows by employing two novel methodologies.

Anahtar Kelimeler

COVID-19, Cryptocurrency, Diebold-Yilmaz Connectedness, Frequency Connectedness

Kriptopara Bağlantılılığı ve COVID-19: Diebold-Yılmaz ve Frekans Bağlantılılığı Yöntemleri

Öz

Finansal/jeopolitik karmaşa dönemlerinde finansal bağlantılılığın yükselme eğiliminde olduğu bilinmektedir. Bu bağlamda çalışma, COVID-19 küresel salgınının finansal sistemin önemli bir bileşeni olan kriptopara piyasası bağlantılılığına olan etkisini Diebold-Yilmaz ve frekans bağlantılılığı yöntemleriyle 02/10/2017-03/01/2021 döneminde incelemektedir. Her iki yöntemle de elde edilen toplam yayılma endeksleri, 2017/2018 kriptopara piyasası balonuna anlamlı bir şekilde tepki vermekte ve yazınla uyumlu olarak COVID-19’un DSÖ tarafından resmi olarak küresel salgın ilan edildiği 2020 Mart döneminde anlamlı bir seviyeye yükselmektedirler. Çalışma en yüksek piyasa işlem hacmine sahip 8 kriptopara arasındaki COVID-19 dönemi bağlantılılığını farklı frekanslarda ve 200-günlük kayan pencerelerde iki yeni metodoloji ile ölçerek literatüre katkı sunmaktadır.

Anahtar Kelimeler

COVID-19, Kriptopara, Diebold-Yılmaz Bağlantılılığı, Frekans Bağlantılılığı

Kaynakça

• Adekoya, O.B. & J.A. Oliyide (2021), “How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques”, Resources Policy, 70(101898), 1-17.
• Akyildirim, E. et al. (2020), “The development of bitcoin futures: Exploring the interactions between cryptocurrency derivatives”, Finance Research Letters, 34(101234), 1-9.
• Antonakakis, N. & D. Gabauer (2017), “Refined Measures of Dynamic Connectedness based on TVP-VAR”, Munich Personal RePEc Archive, No. 78282, University Library of Munich, Almanya.
• Antonakakis, N. & R. Kizys (2015), “Dynamic spillovers between commodity and currency markets”, International Review of Financial Analysis, 41, 303-319.
• Antonakakis, N. et al. (2019), “Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios”, Journal of International Financial Markets, Institutions and Money, 61, 37-51.
• Aslanidis, N. et al. (2020), “Are cryptocurrencies becoming more interconnected?”, Economics Letters, 199, 109725.
• Baek, C. & M. Elbeck (2015), “Bitcoins as an investment or speculative vehicle? A first look”, Applied Economics Letters, 22(1), 30-34.
• Bagheri, E. & S.B. Ebrahimi (2020), “Estimating Network Connectedness of Financial Markets and Commodities”, Journal of Systems Science and Systems Engineering, 29(5), 572-589.
• Baruník, J. & T. Křehlík (2018), “Measuring the frequency dynamics of financial connectedness and systemic risk”, Journal of Financial Econometrics, 16(2), 271-296.
• Baur, D.G. & T. Dimpfl (2018), “Asymmetric volatility in cryptocurrencies”, Economics Letters, 173, 148-151.
• Ben Amar, A. et al. (2021), “Connectedness among regional financial markets in the context of the COVID-19”, Applied Economics Letters, 28(20), 1789-1796.
• Bonga-Bonga, L. (2018), “Uncovering equity market contagion among BRICS countries: an application of the multivariate GARCH model”, The Quarterly Review of Economics and Finance, 67, 36-44.
• Bouri, E. et al. (2021), “Quantile connectedness in the cryptocurrency market”, Journal of International Financial Markets, Institutions and Money, 71(101302), 1-16.
• Buchholz, M. & L. Tonzer (2016), “Sovereign Credit Risk Co‐Movements in the Eurozone: Simple Interdependence or Contagion?”, International Finance, 19(3), 246-268.
• Cheikh, N.B. et al. (2020), “Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models”, Finance Research Letters, 35(101293), 1-9.
• Chiang, T.C. et al. (2007), “Dynamic correlation analysis of financial contagion: Evidence from Asian markets”, Journal of International Money and Finance, 26(7), 1206-1228.
• Chow, H.K. (2020), “Connectedness of Asia Pacific forex markets: China's growing influence”, International Journal of Finance & Economics, 26(3), 3807-3817.
• CoinMarketCap (2021), <https://coinmarketcap.com/all/views/all/>, 06.01.2021.
• Conlon, T. et al. (2020), “Are Cryptocurrencies a Safe Haven for Equity Markets? An International Perspective from the COVID-19 Pandemic”, Research in International Business and Finance, 54(101248), 1-10.
• Corbet, S. et al. (2018), “Exploring the dynamic relationships between cryptocurrencies and other financial assets”, Economics Letters, 165, 28-34.
• Corbet, S. et al. (2019), “Cryptocurrencies as a financial asset: A systematic analysis”, International Review of Financial Analysis, 62, 182-199.
• Costa, A. et al. (2021), “Sectoral connectedness: New evidence from US stock market during COVID-19 pandemics”, Finance Research Letters, (102124), 1-14.
• Dai, X. et al. (2020), “Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach”, Energy Economics, 88(104774), 1-20.
• Dewandaru, G. et al. (2014), “Stock market co-movements: Islamic versus conventional equity indices with multi-timescales analysis”, Economic Systems, 38(4), 553-571.
• Diebold, F.X. & K. Yilmaz (2009), “Measuring financial asset return and volatility spillovers, with application to global equity markets”, The Economic Journal, 119(534), 158-171.
• Diebold, F.X. & K. Yilmaz (2012), “Better to give than to receive: Predictive directional measurement of volatility spillovers”, International Journal of Forecasting, 28(1), 57-66.
• Diebold, F.X. & K. Yilmaz (2014), “On the network topology of variance decompositions: Measuring the connectedness of financial firms”, Journal of Econometrics, 182(1), 119-134.
• Diebold, F.X. & K. Yilmaz (2015), “Trans-Atlantic equity volatility connectedness: US and European financial institutions, 2004-2014”, Journal of Financial Econometrics, 14(1), 81-127.
• Fasanya, I.O. et al. (2021), “Dynamic spillovers and connectedness between COVID-19 pandemic and global foreign exchange markets”, Economic Research-Ekonomska Istraživanja, 34(1), 2059-2084.
• Fernández-Rodríguez, F. et al. (2016), “Using connectedness analysis to assess financial stress transmission in EMU sovereign bond market volatility”, Journal of International Financial Markets, Institutions and Money, 43, 126-145.
• Fousekis, P. & D. Tzaferi (2021), “Returns and volume: Frequency connectedness in cryptocurrency markets”, Economic Modelling, 95, 13-20.
• Fry, J. & E.T. Cheah (2016), “Negative bubbles and shocks in cryptocurrency markets”, International Review of Financial Analysis, 47, 343-352.
• Fry, J. (2018), “Booms, busts and heavy-tails: The story of Bitcoin and cryptocurrency markets?”, Economics Letters, 171, 225-229.
• Ghosh, I. et al. (2021), “Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH”, Computational Economics, 57, 503-527.
• Hafner, C.M. (2020), “Testing for bubbles in cryptocurrencies with time-varying volatility”, Journal of Financial Econometrics, 18(2), 233-249.
• Ji, Q. et al. (2019), “Dynamic connectedness and integration in cryptocurrency markets”, International Review of Financial Analysis, 63, 257-272.
• Jokipii, T. & B. Lucey (2007), “Contagion and interdependence: measuring CEE banking sector co-movements”, Economic Systems, 31(1), 71-96.
• Kostika, E. & N.T. Laopodis (2019), “Dynamic linkages among cryptocurrencies, Exchange rates and global equity markets”, Studies in Economics and Finance, 37(2), 243-265.
• Kyriazis, N. et al. (2020), “A Systematic Review of the Bubble Dynamics of Cryptocurrency Prices”, Research in International Business and Finance, 54, 101254.
• Le, T.L. et al. (2020), “Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution”, Technological Forecasting and Social Change, 162(120382), 1-16.
• Liu, X. et al. (2017), “The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH-BEKK model”, Physica A: Statistical Mechanics and its Applications, 465, 374-383.
• Maghyereh, A.I. et al. (2019), “Connectedness and hedging between gold and Islamic securities: A new evidence from time-frequency domain approaches”, Pacific-Basin Finance Journal, 54, 13-28.
• Mensi, W. et al. (2018), “Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets”, Finance Research Letters, 25, 230-238.
• Naeem, M.A. et al. (2021), “COVID-19 and cryptocurrency market: Evidence from quantile connectedness”, Applied Economics, 1-27.
• Nakamoto, S. (2008), Bitcoin: A peer-to-peer electronic cash system, <https://Bitcoin.org/Bitcoin.pdf/>, 02.01.2020.
• Nofer, M. et al. (2017), “Blockchain”, Business & Information Systems Engineering, 59(3), 183-187.
• Owusu-Junior, P. et al. (2020), “Connectedness of cryptocurrencies and gold returns: Evidence from frequency-dependent quantile regressions”, Cogent Economics & Finance, 8(1), 1804037.
• Polat, O. (2019), “Systemic risk contagion in FX market: A frequency connectedness and network analysis”, Bulletin of Economic Research, 71(4), 585-598.
• Polat, O. (2020), “COVID-19 ve Küresel Finansal Krizi Finansal Risk Bağlantılılığı: Frekans Bağlantılılığı Yöntemi Uygulaması”, İzmir İktisat Dergisi, 35(3), 623-634.
• Reboredo, J.C. & A. Ugolini (2020), “Price connectedness between green bond and financial markets”, Economic Modeling, 88, 25-38.
• Saiti, B. et al. (2016), “Testing the conventional and Islamic financial market contagion: evidence from wavelet analysis”, Emerging Markets Finance and Trade, 52(8), 1832-1849.
• So, M.K. et al. (2020), “Impacts of the COVID-19 pandemic on financial market connectedness”, Finance Research Letters, 101864, 1-8.
• Støve, B. et al. (2014), “Using local Gaussian correlation in a nonlinear re-examination of financial contagion”, Journal of Empirical Finance, 25, 62-82.
• Syllignakis, M.N. & G.P. Kouretas (2011), “Dynamic correlation analysis of financial contagion: Evidence from the Central and Eastern European markets”, International Review of Economics & Finance, 20(4), 717-732.
• Umar, Z. et al. (2020), “The static and dynamic connectedness of environmental, social, and governance investments: International evidence”, Economic Modelling, 93, 112-124.
• Wong, W.S. et al. (2018), Cryptocurrency: A new investment opportunity? An investigation of the hedging capability of cryptocurrencies and their influence on stock, bond and gold portfolios. An Investigation of the Hedging Capability of Cryptocurrencies and Their Influence on Stock, Bond and Gold Portfolios, <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=312737/>, 10.01.2021.
• Yaga, D. et al. (2019), Blockchain technology overview, arXiv preprint arXiv:1906.11078.
• Zheng, Z. et al. (2018), “Blockchain challenges and opportunities: A survey”, International Journal of Web and Grid Services, 14(4), 352-375.