The Volatility Transmission Between Cryptocurrency And Global Stock Market Indices: Case Of Covid-19 Period

Yıl 2022, , 443 - 459, 05.06.2022

Sinem Atıcı Ustalar , Enes Ayar , Selim Şanlısoy

https://doi.org/10.24988/ije.1034580

Cited By: 2

Öz

The uncertainty originated by the COVID-19 pandemic and the unpredictability of both real and financial market indicators have increased the volatility of global financial markets. As a result of globalization, the determination of risk and information transfer between financial markets has gained importance during the pandemic process. In this context, the spread of volatility between the cryptocurrency market and the global stock markets was analyzed by considering the pandemic process. Bitcoin, which represents 42% of the total market cap, was used to represent the cryptocurrency market in the analysis. S&P500, FTSE100, SSEC and NIKKEI indices, which are among the world's leading indices in terms of market cap, were used to represent the global stock market. Constant Conditional Correlation Multivariate GARCH model was used for the analysis of volatility transmission. Daily closing prices covering the date range from 1st December 2019 to 1st July 202 were used for the analyses. The model results were positive and significant for all predicted conditional correlation parameters. In this context, there is volatility transmission and information transfer between BTC and stock returns. The model findings are expected to be a supporting element for financial market participants to make the right decision in the optimal portfolio allocation process.

Anahtar Kelimeler

Cryptocurrencies Market, Stock Market, Volatility Transmission, Constant Conditional Correlation, Multivariate GARCH Model

Kripto Para Ve Küresel Borsa Endeksleri Arasındaki Volatilite Aktarımı: Covid-19 Dönemi Örneği

Öz

COVID-19 pandemisinin yarattığı belirsizlik ortamının yanı sıra hem reel hem de finansal piyasa göstergelerininin öngörülememesi küresel finans piyasalarının oynaklığını arttırmıştır. Küreselleşmenin de bir sonucu olarak, finansal piyasalar arasındaki risk ve bilgi aktarımının belirlenmesi pandemi sürecinde önem kazanmıştır. Bu bağlamda çalışmada, kripto para piyasası ile küresel hisse senedi piyasaları arasındaki oynaklık yayılımı pandemi süreci dikkate alınarak analiz edilmiştir. Analizde kripto para piyasasını temsilen toplam piyasa değerinin %42'sini temsil eden Bitcoin kullanılmıştır. Küresel hisse senedi piyasasını temsil etmesi için ise piyasa değeri açısından dünyanın önde gelen endekslerinden S&P500, FTSE100, SSEC ve NIKKEI endeksleri kullanılmıştır. Oynaklık yayılımının analizi için Sabit Koşullu Korelasyon Çok Değişkenli GARCH modeli kullanılmıştır. Analizlerimiz 1 Aralık 2019 ile 1 Temmuz 2021 tarih aralığını kapsayan günlük kapanış fiyatları kullanılarak gerçekleştirilmiştir. Model sonuçları, tahmin edilen tüm koşullu korelasyon parametrelerinin pozitif ve anlamlı elde edilmiştir. Bu bağlamda, BTC ve hisse senedi getirileri arasında bir oynaklık yayılımı ve bilgi aktarımı vardır. Model bulgularının, finansal piyasa katılımcılarının optimal portföy tahsis sürecinde doğru karar vermeleri için destekleyici bir unsur olması beklenmektedir.

Anahtar Kelimeler

Kripto Para Piyasası, Hisse Senedi Piyasas, Oynaklık Geçişkenliği, Sabit Koşullu Korelasyon, Çok Değişkenli GARCH Modeli

Kaynakça

• Ajmi, H., Arfaoui, N. and Saci, K. (2021). Volatility Transmission Across International Markets Amid COVID 19 pandemic. Studies in Economics and Finance, 38 (5), pp. 926-945. https://dx.doi.org/10.1108/SEF-11-2020-0449.
• Atıcı Ustalar, S. and Şanlısoy, S. (2021). COVID-19 Krizi’nin Türkiye ve G7 ülkelerinin borsa oynaklıkları üzerindeki etkisi, Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 16(2), 446–462. https://dergipark.org.tr/en/download/article-file/1594564.
• Aydın, Ü. and Yıldız, S. N., (2022). Covid-19 Salgınının Türkiye’de Finansal Yatırım Araçları Üzerindeki Etkisi, Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23(1), pp. 294-316.
• Bala, D. A. and Takimoto, T. (2017). Stock Markets Volatility Spillovers During Financial Crises: A DCC-MGARCH With Skewed-T Density Approach. Borsa Istanbul Review, 17 (1), 25-48. https://dx.doi.org/10.1016/j.bir.2017.02.002.
• Bilik, M. and Aydın, Ü. (2021), Effect of Covid-19 on financial markets, Ç. Başarir and B. Darici (Ed.), Financial Systems, Central Banking, and Monetary Policy during COVID-19 Pandemic and After included (19-35), Lexington Books, London, United Kingdom.
• Bitcoinity.org. (2021, November 26). Bitcoin trading volume. https://data.bitcoinity.org/markets/volume/all?c=e&t=b.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://dx.doi.org/10.1016/0304-4076(86)90063-1.
• Bollerslev, T. (1987). A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return. The Review of Economics and Statistics, 69(3),542-547. https://dx.doi.org/10.2307/1925546.
• Bollerslev, T. (1990). Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. The Review of Economics and Statistics, 72(3), 498-505. https://dx.doi.org/10.2307/2109358.
• Coinmarketcap. (2021, November 26). Total Cryptocurrency Market Cap https://coinmarketcap.com/charts/.
• Corbet, S., Larkin, C. and Lucey, B. (2020). The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies. Finance Research Letters, 35, 1-7. https://dx.doi.org/10.1016/j.frl.2020.101554
• Dickey, D. A. and W. A. Fuller (1979). Distribution of Estimators of Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, 427–431. https://www.jstor.org/stable/2286348?seq=1#metadata_info_tab_contents.
• Diebold, F. X. and Yilmaz, K. (2008). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119 (534), 158-171. https://dx.doi.org/10.1111/j.1468-0297.2008.02208.x.
• Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, 16, 139-144. https://dx.doi.org/10.1016/j.frl.2015.10.025.
• Ghorbel, A., and Jeribi, A. (2021). Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period. Eurasian Economic Review, 11, 449-467. https://dx.doi.org/10.1007/s40822-021-00181-6.
• Huang, Y., Duan, K. and Mishra, T. (2021). Is Bitcoin really more than a diversifier? A pre- and post-COVID-19 analysis. Finance Research Letters, 43. https://dx.doi.org/10.1016/j.frl.2021.102016.
• IMF. (2021, October 28). Policy Responses to Covid-19. International Monetary Fund. Retreived from https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19#T.
• Kallner, A. (2018). Laboratory Statistics (Second Edition). Elsevier. https://dx.doi.org/10.1016/C2017-0-00959-X.
• Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6. https://dx.doi.org/10.1016/j.econlet.2017.06.023.
• Kwiatkowski, D., Philips, P., Schmidt, P., and Shin, Y. (1992). Testing the null hypothesis of stationary against the alternative of unit root. Journal of Econometrics, 54, 159–178. http://wexler.free.fr/library/files/kwiatowski%20(1992)%20testing%20the%20null%20hypothesis%20of%20stationarity%20against%20the%20alternative%20of%20a%20unit%20root.pdf.
• Luo, J. and Wang, S. (2019). The asymmetric high-frequency volatility transmission across international stock markets. Finance Research Letters, 31, 104-109. https://dx.doi.org/10.1016/j.frl.2019.04.025.
• OECD. (2020, March). Global Financial Markets Policy Responses to COVID-19. Retrieved from https://www.oecd.org/coronavirus/policy-responses/global-financial-markets-policy-responses-to-covid-19-2d98c7e0/.
• Phillips, P.C.B. & P. Perron (1988). Testing for a unit root in time series regression. Biometrika, 75, 335–346. https://www.jstor.org/stable/2336182?seq=1#metadata_info_tab_contents.
• Schell, D., Wang, M. and Huynh, T. L. (2020). This time is indeed different: A study on global market reactions to public health crisis. Journal of Behavioral and Experimental Finance, 27, 100349. https://dx.doi.org/10.1016/j.jbef.2020.100349.
• Shahzad, S. J., Bouri, E., Roubaud, D., Kristoufek, L. and Lucey, B. (2019). Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322-330. https://dx.doi.org/10.1016/j.irfa.2019.01.002.
• Trading hours. (2021, September 7) Retrieved from https://www.tradinghours.com/markets.
• Tse, Y. K. (2000). A test for constant correlations in a multivariate GARCH model. Journal of Econometrics, 98(1), 107-127. https://dx.doi.org/10.1016/S0304-4076(99)00080-9
• Ural, M. and Demireli, E. (2015). Volatility transmission of credit default swap (cds) risk premiums. Dumlupınar University Journal of Social Sciences, 45,24-33. https://dergipark.org.tr/en/download/article-file/56094.
• Uzonwanne, G. (2021). Volatility and return spillovers between stock markets and cryptocurrencies. The Quarterly Review of Economics and Finance, 82, 30-36. https://dx.doi.org/10.1016/j.qref.2021.06.018.
• Yousaf, I. and Ali, S. (2020). The COVID-19 outbreak and high frequency information transmission between major cryptocurrencies: Evidence from the VAR-DCC-GARCH approach. Borsa Istanbul Review, 20 (Suppl. 1), 1-10. https://dx.doi.org/10.1016/j.bir.2020.10.003.
• Zhang, D., Hu, M. and Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36, 1-6. https://dx.doi.org/10.1016/j.frl.2020.101528.