Financial Connectedness among Credit Default Swaps

Kredi temerrüt swapı piyasalara ilişkin bir risk göstergesi olarak kabul edilmekte ve piyasanın risk algısını yansıtmaktadır. Bu nedenle kredi temerrüt swapları endekslerindeki değişimler yatırımcılar, finansal kurumlar ve politika yapıcılar tarafından yakından takip edilmektedir. Kredi temerrüt swaplarında meydana gelen değişimler sadece iç dinamiklerden değil, aynı zamanda risk geçişlerine bağlı olarak diğer kredi temerrüt swaplarındaki oynaklıklardan kaynaklanabilmektedir. Diebold ve Yilmaz kendi metodolojilerini geliştirdikleri çalışmalarında oynaklık yayılımlarını “finansal bağlantılılık” terimini kullanarak incelemiştir. Oynaklık yayılımları her ne kadar kriz dönemlerinde artsa da, yayılımlar normal finansal koşullar altında da gerçekleşebilmektedir. Bu nedenle risk yönetimi sürecinde oynaklık yayılımlarının önceden tespit edilmesi oldukça önemlidir. Bu bağlamda çalışmanın temel amacı 15.09.2008 – 26.04.2019 tarihleri arasında Arjantin, Belçika, Çin, Danimarka, Norveç, Polonya ve Türkiye ülkeleri kredi temerrüt swapları arasındaki finansal bağlantılılığın Diebold ve Yilmaz (2012) tarafından geliştirilen yöntem ile analiz edilmesidir. Çalışmada, ele alınan ülke kredi temerrüt swapları arasında belirli düzeyde finansal bağlantılılık olduğu tespit edilmiştir.

Financial Connectedness among Credit Default Swaps

Credit default swap are considered as a risk indicator for the markets and reflect the risk perception of the market. For this reason, changes in credit default swaps indices are closely followed by investors, financial institutions and policy makers. Changes in credit default swaps are not caused only by internal dynamics, the volatility of other credit default swaps may cause changes due to the risk transmissions. Diebold and Yilmaz examined the volatility relationships by using the term “financial connectedness” in their studies in which they developed their own methodologies. Although they increase in the crisis periods, volatility spillovers may occur in stable financial conditions. For this reason, it is very important to determine the volatility spillovers in the risk management process in advance. In this context, the purpose of this study is to analyze the financial connectedness among credit default swaps of Argentina, Belgium, China, Denmark, Norway, Poland and Turkey by using the methodology developed by Diebold and Yilmaz (2012), for the period of 15.09.2008 – 26.04.2019. It is determined by the study that there is financial connectedness between credit default swap of the selected countries. 

___

  • Adam, M. (2013). Spillovers and contagion in the sovereign CDS market. Bank i Kredyt, 44(6), 571-604.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • Bollerslev, T., Engle, R.F. ve Nelson, D.B. (1994), Arch Models, içinde Englen, R.F. ve McFadden, D. (Ed.), The Handbook of Econometrics, Amsterdam, 2959-3038.
  • Bostanci, G., & Yilmaz, K. (2015). How connected is the global sovereign credit risk network? Koç University-TÜSİAD Economıc Research Forum, Working Paper 1515.
  • Bouoiyour, J., & Selmi, R. (2018). Brexit and CDS spillovers across UK and Europe. The European Journal of Comparative Economics, Vol. 16, no. 1, 105-124.
  • Buchholz, M., & Tonzer, L. (2016). Sovereign Credit Risk Co‐Movements in the Eurozone: Simple Interdependence or Contagion? International Finance, 19(3), 246-268.
  • Da Fonseca, J., & Gottschalk, K. (2018). The Co‐Movement of Credit Default Swap Spreads, Equity Returns and Volatility: Evidence from Asia‐Pacific Markets. International Review of Finance. DOI: 10.1111/irfi.12237
  • Da Fonseca, J., & Ignatieva, K. (2018). Volatility spillovers and connectedness among credit default swap sector indexes. Applied Economics, 50(36), 3923-3936.
  • Diebold, F.X. & K. Yilmaz (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. Economic Journal, 119, 158-171.
  • Diebold, F.X. & K. Yilmaz (2012). Better to Give than to Receive: Predictive Measurement of Volatility Spillovers. International Journal of Forecasting, 28, 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, 119-134.
  • Hassan, M. K., Ngow, T. S., Yu, J. S., & Hassan, A. (2013). Determinants of credit default swaps spreads in European and Asian markets. Journal of Derivatives & Hedge Funds, 19(4), 295-310.
  • Holden, C. W., Jacobsen, S., & Subrahmanyam, A. (2014). The empirical analysis of liquidity. Foundations and Trends in Finance, 8(4), 263-365.
  • Kamışlı, S. & Esen, E. (2018) “Avrupa Ülkeleri CDS Endeksleri Arasındaki Oynaklık Yayılımlarının Analizi”. IMASCON 2018 Uluslararası Marmara Fen ve Sosyal Bilimler Kongresi, 23 – 25 Kasım, Kocaeli, Türkiye.
  • Kayalar, D. E., Talaslı, I., & Ünalmış, I. (2017). Interdependencies across Sovereign Bond Credit Default Swap Markets. Central Bank of the Republic of Turkey, Working Paper No: 17/07.
  • Meng, L., ap Gwilym, O., & Varas, J. (2009). Volatility transmission among the CDS, equity, and bond markets. The Journal of Fixed Income, 18(3), 33-46. Mengle, D. (2007) Credit derivatives: An overview. Economic Review, 92(4), 1–24.
  • Nikkinen, J., Omran, M.M., Sahlstrom, P. & Aijo, J. (2008). Stock returns and volatility following the September 11 attacks: evidence from 53 equity markets. International Review of Financial Analysis, Vol. 17 No. 1: 27-46.
  • Pu, X., & Zhang, J. (2012). Sovereign cds spreads, volatility, and liquidity: Evidence from 2010 german short sale ban. Financial Review, 47(1), 171-197.
  • Rahim, S. & Ahmad, N. (2016) “Measuring Volatility Of Dow Jones Sukuk Total Return Index Using Garch Model”, Journal of Business Innovation, 1(1): 73-88.
  • Ramlall, I. (2010). Has the US subprime crisis accentuated volatility clustering and leverage effects in major international stock markets? International Research Journal of Finance and Economics, Vol. 39: 157-185.
  • Rejeb, A. B. & Arfoui, M. (2019). Do Islamic stock indexes outperform conventional stock indexes? A state space modeling approach. European Journal of Management and Business Economics, DOI 10.1108/EJMBE-08-2018-0088
  • Shino, J., & Takahashi, K. (2010). Sovereign credit default swaps: Market developments and factors behind price changes. Bank of Japan Review, 1-9.
  • Taly, I. (2015). Study on return and volatility spillover effects among stock, CDS, and foreign exchange markets in Korea. East Asian Economic Review, 19(3), 275-322.
  • Tamakoshi, G., & Hamori, S. (2014). Spillovers among CDS indexes in the US financial sector. The North American Journal of Economics and Finance, 27, 104-113.
  • Tamakoshi, G., & Hamori, S. (2016). Time-varying co-movements and volatility spillovers among financial sector CDS indexes in the UK. Research in International Business and Finance, 36, 288-296.
  • Tokat, H. A. (2013). Understanding volatility transmission mechanism among the cds markets: Europe & North America versus Brazil & Turkey. Economia Aplicada, 17(1), 5-19.
  • Ural, M., & Demireli, E. (2015). Volatility Transmission of Credit Default Swap (CDS) Rısk Premiums. Dumlupınar University Journal of Social Science, (45).
  • Weithers, T. (2007) Credit derivatives, macro risks, and systemic risks. Economic Review 92(4), 43–69.