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TVP-VAR Based CARR-Volatility Connectedness: Evidence from The Russian-Ukraine Conflict

Yıl 2022, Cilt: 7 Sayı: 3, 590 - 607, 30.09.2022
https://doi.org/10.30784/epfad.1138999

Öz

This paper aims to examine the spillover between volatilities obtained from the Conditional Autoregressive Range (CARR) process with the Time-Varying Parameter Vector Autoregressive (TVP-VAR) based Diebold-Yilmaz approach. We apply Gumbel distributed CARR (1,1) to estimate the volatilities. The summary statistics for the volatility series indicate that the series are not normally distributed, and innovations fit the Gumbel distribution. Also, the obtained volatility series are stationary. We also observe that a significant autocorrelation emerges in all series and the square series. Therefore, using a TVP-VAR model with a time-varying variance-covariance structure is a proper econometric framework to capture all these empirical properties. Moreover, we investigate the impact of the Ukraine-Russia Conflict on global markets as an example. For this purpose, we consider the Russian stock market index and indices selected from among the twenty largest stock exchanges by asset size to perform the connectedness analysis. In TVP-VAR based connectedness approach, we calculate averaged connectedness measures of two panels, without and with the Russian stock exchange. The findings show that the total connectedness index is 79.91% in the first panel, and it increases to 81.44% with the addition of Russian market.

Kaynakça

  • 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. https://doi.org/10.3390/jrfm13040084
  • Antonakakis, N., Gabauer, D., Gupta, R. and Plakandaras, V. (2018). Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics Letters, 166, 63-75. doi:10.1016/j.econlet.2018.02.011
  • Ari, Y. (2020). Volatility transmission model using DCC-GARCH representation. In S. Evci and A. Sharma (Eds.), Studies at the crossroads of management & economics (pp. 237-250). London: IJOPEC Publication.
  • Ari, Y. (2022). From discrete to continuous: GARCH volatility modeling of the Bitcoin. Ege Academic Review, 22(3), 353-370. doi:10.21121/eab.819934
  • Boubaker, S., Goodell, J.W., Pandey, D.K. and Kumari, V. (2022). Heterogeneous impacts of wars on global equity markets: Evidence from the invasion of Ukraine. Finance Research Letters, 48, 102934. doi:10.1016/j.frl.2022.102934
  • Boungou, W. and Yatié, A. (2022). The impact of the Ukraine–Russia war on world stock market returns. Economics Letters, 215, 110516. doi:10.1016/j.econlet.2022.110516
  • 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
  • Chou, R.Y. (2005). Forecasting financial volatilities with extreme values: The conditional autoregressive range (CARR) model. Journal of Money, Credit and Banking, 37, 561-582. doi:10.1353/mcb.2005.0027
  • Davidovic, M. (2021). From pandemic to financial contagion: High-frequency risk metrics and Bayesian volatility analysis. Finance Research Letters, 42, 101913. doi:10.1016/j.frl.2020.101913
  • Demiralay, S. and Bayraci, S. (2015). Central and Eastern European stock exchanges under stress: A range-based volatility spillover framework. Finance a Uver: Czech Journal of Economics & Finance, 65(5), 411-430. Retrieved from https://journal.fsv.cuni.cz/
  • Diebold, F.X. and Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119, 158-171. doi:10.1111/j.1468-0297.2008.02208.x
  • Diebold, F.X. and Yilmaz, K. (2012). Better to give than to receive: Predictive measurement of volatility spillovers. International Journal of Forecasting, 28, 57-66. doi:10.1016/j.ijforecast.2011.02.006
  • Diebold, F.X. and Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182, 119-134. doi:10.1016/j.jeconom.2014.04.012
  • Diebold, F.X. and Yilmaz, K. (2015). Financial and macroeconomic connectedness: A network approach to measurement and monitoring. New York: Oxford University Press. doi:10.1093/acprof:oso/9780199338290.001.0001
  • Guo, Y., Li, P. and Li, A. (2021). Tail risk contagion between international financial markets during COVID-19 pandemic. International Review of Financial Analysis, 73, 101649. doi:10.1016/j.irfa.2020.101649
  • Koop, G. and Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101-116. https://doi.org/10.1016/j.euroecorev.2014.07.002
  • Korobilis, D. and Yilmaz, K. (2018). Measuring dynamic connectedness with large Bayesian VAR models (Koç University TÜSİAD Economic Research Forum (ERF), Working Paper, No. 1802). Retrieved from https://www.econstor.eu/bitstream/10419/202976/1/1011071614.pdf
  • Liu, Y., Wei, Y., Wang, Q. and Liu, Y. (2022). International stock market risk contagion during the COVID-19 pandemic. Finance Research Letters, 45, 102145. doi:10.1016/j.frl.2021.102145
  • Umar, Z., Polat, O., Choi, S.Y. and Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Finance Research Letters, 102976. doi:10.1016/j.frl.2022.102976
  • Yousaf, I., Patel, R. and Yarovaya, L. (2022). The reaction of G20+ stock markets to the Russia-Ukraine conflict (SSRN Paper No. 4069555). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4069555

TVP-VAR Tabanlı CARR Oynaklık Bağlantılılığı: Rusya-Ukrayna Çatışmasından Kanıtlar

Yıl 2022, Cilt: 7 Sayı: 3, 590 - 607, 30.09.2022
https://doi.org/10.30784/epfad.1138999

Öz

Bu çalışma Zamanla Değişen Parametreli Vektör Otoregresif (TVP-VAR) tabanlı Diebold-Yılmaz yaklaşımı ile Koşullu Otoregresif Aralık (CARR) sürecinden elde edilen oynaklıklar arasındaki yayılmayı incelemeyi amaçlamaktadır. Çalışmada volatiliteleri tahmin etmek için Gumbel olasılık dağılımına sahip CARR (1,1) uygulanmıştır. Özet istatistikler serilerin normal dağılım göstermediğini ve inovasyonların Gumbel dağılımına uyduğunu göstermektedir. Ayrıca elde edilen oynaklık serileri durağandır. Bunların yanında tüm serilerde ve kare serilerde anlamlı bir otokorelasyonun ortaya çıktığı gözlemlenmiştir. Bu nedenle, zamanla değişen varyans-kovaryans yapısına sahip bir TVP-VAR modeli tüm bu ampirik özellikleri yakalamak için uygun bir ekonometrik çerçevedir. Metodolojik yaklaşıma örnek olarak Ukrayna-Rusya Savaşının küresel piyasalar üzerindeki etkisini ortaya koyan bir uygulama sunulmuştur. Bu amaçla, bağlantılılık analizini gerçekleştirmek için varlık büyüklüğüne göre küresel ölçekte en büyük yirmi borsa arasından seçilen endeksler ile Rus borsa endeksi verisini içeren TVP-VAR analizi iki gruba ayrılmıştır. İlk grubu oluşturan panelde Rus borsa endeksinin oynaklığı dahil edilmezken, ikinci panele dahil edilerek ortalama toplam bağlantılılık endeksleri hesaplanmıştır. Bulgular, toplam bağlantılılık endeksinin ilk panelde %79,91 olduğunu ve Rusya pazarının eklenmesiyle %81,44'e yükseldiğini göstermektedir.

Kaynakça

  • 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. https://doi.org/10.3390/jrfm13040084
  • Antonakakis, N., Gabauer, D., Gupta, R. and Plakandaras, V. (2018). Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics Letters, 166, 63-75. doi:10.1016/j.econlet.2018.02.011
  • Ari, Y. (2020). Volatility transmission model using DCC-GARCH representation. In S. Evci and A. Sharma (Eds.), Studies at the crossroads of management & economics (pp. 237-250). London: IJOPEC Publication.
  • Ari, Y. (2022). From discrete to continuous: GARCH volatility modeling of the Bitcoin. Ege Academic Review, 22(3), 353-370. doi:10.21121/eab.819934
  • Boubaker, S., Goodell, J.W., Pandey, D.K. and Kumari, V. (2022). Heterogeneous impacts of wars on global equity markets: Evidence from the invasion of Ukraine. Finance Research Letters, 48, 102934. doi:10.1016/j.frl.2022.102934
  • Boungou, W. and Yatié, A. (2022). The impact of the Ukraine–Russia war on world stock market returns. Economics Letters, 215, 110516. doi:10.1016/j.econlet.2022.110516
  • 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
  • Chou, R.Y. (2005). Forecasting financial volatilities with extreme values: The conditional autoregressive range (CARR) model. Journal of Money, Credit and Banking, 37, 561-582. doi:10.1353/mcb.2005.0027
  • Davidovic, M. (2021). From pandemic to financial contagion: High-frequency risk metrics and Bayesian volatility analysis. Finance Research Letters, 42, 101913. doi:10.1016/j.frl.2020.101913
  • Demiralay, S. and Bayraci, S. (2015). Central and Eastern European stock exchanges under stress: A range-based volatility spillover framework. Finance a Uver: Czech Journal of Economics & Finance, 65(5), 411-430. Retrieved from https://journal.fsv.cuni.cz/
  • Diebold, F.X. and Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119, 158-171. doi:10.1111/j.1468-0297.2008.02208.x
  • Diebold, F.X. and Yilmaz, K. (2012). Better to give than to receive: Predictive measurement of volatility spillovers. International Journal of Forecasting, 28, 57-66. doi:10.1016/j.ijforecast.2011.02.006
  • Diebold, F.X. and Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182, 119-134. doi:10.1016/j.jeconom.2014.04.012
  • Diebold, F.X. and Yilmaz, K. (2015). Financial and macroeconomic connectedness: A network approach to measurement and monitoring. New York: Oxford University Press. doi:10.1093/acprof:oso/9780199338290.001.0001
  • Guo, Y., Li, P. and Li, A. (2021). Tail risk contagion between international financial markets during COVID-19 pandemic. International Review of Financial Analysis, 73, 101649. doi:10.1016/j.irfa.2020.101649
  • Koop, G. and Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101-116. https://doi.org/10.1016/j.euroecorev.2014.07.002
  • Korobilis, D. and Yilmaz, K. (2018). Measuring dynamic connectedness with large Bayesian VAR models (Koç University TÜSİAD Economic Research Forum (ERF), Working Paper, No. 1802). Retrieved from https://www.econstor.eu/bitstream/10419/202976/1/1011071614.pdf
  • Liu, Y., Wei, Y., Wang, Q. and Liu, Y. (2022). International stock market risk contagion during the COVID-19 pandemic. Finance Research Letters, 45, 102145. doi:10.1016/j.frl.2021.102145
  • Umar, Z., Polat, O., Choi, S.Y. and Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Finance Research Letters, 102976. doi:10.1016/j.frl.2022.102976
  • Yousaf, I., Patel, R. and Yarovaya, L. (2022). The reaction of G20+ stock markets to the Russia-Ukraine conflict (SSRN Paper No. 4069555). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4069555
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm Makaleler
Yazarlar

Yakup Arı 0000-0002-5666-5365

Yayımlanma Tarihi 30 Eylül 2022
Kabul Tarihi 9 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 7 Sayı: 3

Kaynak Göster

APA Arı, Y. (2022). TVP-VAR Based CARR-Volatility Connectedness: Evidence from The Russian-Ukraine Conflict. Ekonomi Politika Ve Finans Araştırmaları Dergisi, 7(3), 590-607. https://doi.org/10.30784/epfad.1138999