Gelişmekte Olan Piyasalarda Finansal Bağlantılılık

Bu çalışma, gelişmekte olan E7 ülke borsaları (Brezilya, Rusya, Hindistan, Çin, Endonezya, Meksika ve Türkiye) arasındaki finansal bağlantılılığı incelemeyi amaçlamaktadır. Bu amaç doğrultusunda 02 Temmuz 1997-24 Haziran 2020 dönemi için E7 ülke borsaları arasındaki finansal bağlantılılık Diebold ve Yılmaz (2009,2012) yöntemi ile incelenmiştir. Çalışmada elde edilen bulgular, E7 ülke borsaları arasındaki toplam volatilite yayılım endeksinin düşük seviyede olduğunu ortaya koymuştur. Özellikle diğer ülkelere doğru en yüksek finansal risk geçişinin ve risk alışının sırasıyla Meksika ve Brezilya borsalarından kaynaklandığı görülmüştür. Diğer yandan Çin borsası, diğer altı ülke piyasalarına göre risk yayma ve risk alma noktasında düşük seviyede kalmıştır. Ayrıca toplam volatilite endeksi 200 günlük kayan pencereler yaklaşımına göre incelenmiş ve E7 ülke borsaları arasındaki finansal risk geçişinin en yüksek olduğu dönem COVID-19 pandemi dönemi olarak tespit edilmiştir. Çalışmadan elde edilen bulgular dikkate alındığında çalışmanın portföy çeşitlendirmesinde ve piyasa volatilitesinin tahmin edilmesi için portföy yöneticileri ve piyasa düzenleyiciler için önem arz ettiği düşünülmektedir

Financial Connectedness in Emerging Markets

This study aims to examine the financial connectedness between emerging E7 (Brazil, Russia, India, China, Indonesia, Mexico and, Turkey) stock markets. For this purpose, the connectedness between E7 stock markets was examined using the Diebold and Yilmaz (2009,2012) method for the period of 02 July 1997 - 24 June 2020. The findings of the study revealed that the total volatility spillover index among E7 country stock markets was low level. It was seen that the highest financial risk transition and risk receiving to other countries originated from the Mexican and Brazilian stock markets, respectively. On the other hand, the Chinese stock market remained at a low level in terms of risk transmission and risk receiving compared to the markets of the other six countries. In addition, the total volatility index was analyzed according to the 200-day rolling windows approach and the highest financial risk transition between E7 country stock markets was determined as the COVID-19 pandemic period. However, according to the 200-day rolling windows approach, the period in which the financial risk transition between E7 country stock markets is the highest indicates the COVID-19 pandemic period. Considering the findings obtained from the study, it is thought that the study is important for portfolio managers and market regulators in portfolio diversification and for estimating market volatility.

___

  • Abbas, G., Hammoudeh, S., Shahzad, S. J. H., Wang, S. ve Wei, Y. (2019). Return and volatility connectedness between stock markets and macroeconomic factors in the G-7 countries. Journal of Systems Science and Systems Engineering, 28(1), 1-36. doi:10.1007/s11518-018-5371-y
  • Alom, F., Ward, B. ve Hu, B. (2011). Cross country mean and volatility spillover effects of food prices: Multivariate GARCH analysis. Economics Bulletin, 31(2), 1439-1450.
  • Alotaibi, A. R. ve Mishra, A. V. (2015). Global and regional volatility spillovers to GCC stock markets. Economic Modelling, 45, 38-49. doi:10.1016/j.econmod.2014.10.052
  • Aloui, C. (2011). Latin American stock markets’ volatility spillovers during the financial crises: A multivariate FIAPARCH-DCC framework. doi:10.1080/17520843.2011.590597
  • Awartani, B., Aktham, M. ve Cherif, G. (2016). The connectedness between crude oil and financial markets: Evidence from implied volatility indices. Journal of Commodity Markets, 4(1), 56-69. doi:10.1016/j.jcomm.2016.11.002
  • Baele, L. (2005). Volatility spillover effects in European equity markets. The Journal of Financial and Quantitative Analysis, 40(2), 373-401.
  • Bala, D. A. ve 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. doi:10.1016/j.bir.2017.02.002
  • Balcılar, M., Özdemir, Z. A. ve Özdemir, H. (2019). Dynamic return and volatility spillovers among S&P 500, crude oil, and gold. International Journal of Finance & Economics, n/a(n/a). doi:10.1002/ijfe.1782
  • Baruník, J., Kočenda, E. ve Vácha, L. (2016). Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27, 55-78. doi:10.1016/j.finmar.2015.09.003
  • Beirne, J., Caporale, G. M., Schulze-Ghattas, M. ve Spagnolo, N. (2013). Volatility spillovers and contagion from mature to emerging stock markets. Review of International Economics, 21(5), 1060-1075. doi:10.1111/roie.12091
  • Ben Rejeb, A. (2016, Temmuz). Volatility Spillover between Islamic and conventional stock markets: Evidence from quantile regression analysis. MPRA Paper. 10 Ağustos 2020 tarihinde https://mpra.ub.uni-muenchen.de/73302/ adresinden erişildi.
  • Çamlıca, F., Güneş, D. ve Özen, E. (2017). A financial connectedness analysis for Turkey.
  • Cardona, L., Gutiérrez, M. ve Agudelo, D. A. (2017). Volatility transmission between US and Latin American stock markets: Testing the decoupling hypothesis. Research in International Business and Finance, 39, 115-127. doi:10.1016/j.ribaf.2016.07.008
  • Chancharoenchai, K. ve Dibooglu, S. (2006). Volatility spillovers and contagion during the Asian crisis: Evidence from six Southeast Asian stock markets. Emerging Markets Finance and Trade, 42(2), 4–17.
  • Chow, H. K. (2017). Volatility Spillovers and Linkages in Asian Stock Markets. Emerging Markets Finance and Trade, 53(12), 2770-2781. doi:10.1080/1540496X.2017.1314960
  • Christiansen, C. (2007). Volatility-spillover effects in European bond markets. European Financial Management, 13(5), 923-948. doi:10.1111/j.1468- 036X.2007.00403.x
  • Diebold, F. X. ve Yılmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171. doi:10.1111/j.1468-0297.2008.02208.x
  • Diebold, F. X. ve Yılmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, Special Section 1: The Predictability of Financial Markets, 28(1), 57- 66. doi:10.1016/j.ijforecast.2011.02.006
  • Do, A., Powell, R., Yong, J. ve Singh, A. (2019). Time-varying asymmetric volatility spillover between global markets and China’s A, B and H-shares using EGARCH and DCC-EGARCH models. The North American Journal of Economics and Finance, 101096. doi:10.1016/j.najef.2019.101096
  • Du, X., Yu, C. L. ve Hayes, D. J. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33(3), 497-503. doi:10.1016/j.eneco.2010.12.015
  • Faizulayev, A. ve Wada, I. (2019). Spillover Effect of Interest Rate Volatility on Banking Sector Development in Nigeria: Dynamic ARDL Bound Test Approach. N. Ozatac ve K. K. Gokmenoglu (Ed.), Global Issues in Banking and Financeiçinde (s. 111-125). Springer Proceedings in Business and Economics Cham: Springer International Publishing. doi:10.1007/978-3-030- 30387-7_8
  • Gamba-Santamaria, S., Gomez-Gonzalez, J. E., Hurtado-Guarin, J. L. ve Melo-Velandia, L. F. (2017). Stock market volatility spillovers: Evidence for Latin America. Finance Research Letters, 20, 207-216. doi:10.1016/j.frl.2016.10.001
  • Gong, X.-L., Liu, X.-H., Xiong, X. ve Zhang, W. (2019). Financial systemic risk measurement based on causal network connectedness analysis. International Review of Economics & Finance, 64, 290-307. doi:10.1016/j.iref.2019.07.004
  • Jeong, D. ve Park, S. (2018). The more connected, the better? Impact of connectedness on volatility and price discovery in the Korean financial sector. Managerial Finance, 44(1), 46-73. doi:10.1108/MF-09-2016-0277
  • Joshi, P. (2011). Return and volatility spillovers among Asian stock markets. SAGE Open, 1(1), 2158244011413474. doi:10.1177/2158244011413474
  • Kamışlı, M., Kamışlı, S. ve Temizel, F. (2019). Empirical evidence of the relationships between bitcoin and stock exchanges: Case of return and volatility spillover. U. Hacioglu (Ed.), Blockchain Economics and Financial Market Innovation: Financial Innovations in the Digital Age içinde (s. 293-318). , Contributions to Economics Cham: Springer International Publishing. doi:10.1007/978-3-030-25275-5_15
  • Karali, B. ve Ramirez, O. A. (2014). Macro determinants of volatility and volatility spillover in energy markets. Energy Economics, 46, 413-421. doi:10.1016/j.eneco.2014.06.004
  • Kırkulak Uludag, B. ve Khurshid, M. (2019). Volatility spillover from the Chinese stock market to E7 and G7 stock markets. Journal of Economic Studies, 46(1), 90-105. doi:10.1108/JES-01-2017-0014
  • Koop, G., Pesaran, M. H. ve Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119-147. doi:10.1016/0304-4076(95)01753-4
  • Korkmaz, T., Çevik, E. İ. ve Atukeren, E. (2012). Return and volatility spillovers among CIVETS stock markets. Emerging Markets Review, 13(2), 230-252. doi:10.1016/j.ememar.2012.03.003
  • Kumar, A. S. ve Kamaiah, B. (2017). Returns and volatility spillover between Asian equity markets: A wavelet approach. Economic Annals, 62(212), 63–83.
  • Lee, J. (2009). Currency risk and volatility spillover in emerging foreign exchange markets (SSRN Scholarly Paper No: ID 1650049). Rochester, NY: Social Science Research Network. doi:10.2139/ssrn.1650049
  • Li, Y. ve Giles, D. E. (2015). Modelling volatility spillover effects between developed stock markets and Asian Emerging stock markets. International Journal of Finance & Economics, 20(2), 155-177. doi:10.1002/ijfe.1506
  • Liow, K. H. ve Huang, Y. (2018). The dynamics of volatility connectedness in international real estate investment trusts. Journal of International Financial Markets, Institutions and Money, 55, 195-210. doi:10.1016/j.intfin.2018.02.003
  • Mensi, W., Boubaker, F. Z., Al-Yahyaee, K. H. ve Kang, S. H. (2018). Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets. Finance Research Letters, 25, 230-238. doi:10.1016/j.frl.2017.10.032
  • Mishra, A. (2019). Crude oil, stock market, and foreign exchange return volatility and spillover: A GARCH DCC analysis of Indian and Japanese financial market. International Journal of Business Innovation and Research, 20(1), 25- 46. doi:10.1504/IJBIR.2019.101687
  • Nazlıoglu, S., Erdem, C. ve Soytaş, U. (2013). Volatility spillover between oil and agricultural commodity markets. Energy Economics, 36, 658-665. doi:10.1016/j.eneco.2012.11.009
  • Ng, A. (2000). Volatility spillover effects from Japan and the US to the Pacific– Basin. Journal of International Money and Finance, 19(2), 207-233. doi:10.1016/S0261-5606(00)00006-1
  • Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. The Journal of Business, 53(1), 61-65.
  • Pesaran, H. H. ve Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29. doi:10.1016/S0165- 1765(97)00214-0
  • Polat, O. (2018). Hisse senedi piyasalarında finansal bağlantılılık analizi. Politik Ekonomik Kuram, 2(1), 73–86.
  • Priya, S. (2008). Volatility spillover in bullion and energy futures and spot markets. Journal of Emerging Financial Markets, 1(1), 85–107.
  • Qarni, M. O., Gulzar, S., Fatima, S. T., Khan, M. J. ve Shafi, K. (2019). Inter-markets volatility spillover in U.S. bitcoin and financial markets. Journal of Business Economics and Management, 20(4), 694-714. doi:10.3846/jbem.2019.8316
  • Qayyum, A. ve Kemal, A. R. (2006). Volatility spillover between the stock market and the foreign exchange market in Pakistan (SSRN Scholarly Paper No: ID 963308). Rochester, NY: Social Science Research Network. doi:10.2139/ssrn.963308
  • Reboredo, J. C. ve Ugolini, A. (2020). Price connectedness between green bond and financial markets. Economic Modelling, 88, 25-38. doi:10.1016/j.econmod.2019.09.004
  • Singh, P., Kumar, B. ve Pandey, A. (2010). Price and volatility spillovers across North American, European and Asian stock markets. International Review of Financial Analysis, 19(1), 55-64. doi:10.1016/j.irfa.2009.11.001
  • Toraman, C., İğde, M., Buğan, M. F. ve Kılıç, Y. (2016). Volatility spillover effect from conventional stock markets to Islamic stock markets. International Journal of Academic Research in Economics and Management Sciences, 5(4), 2226–3624.
  • Umer, U. M., Coskun, M. ve Kiraci, K. (2018). Time-varying return and volatility spillover among eagles stock markets: A multivariate garch analysis. Journal of Finance and Economics Research, 3(1), 23–42.
  • Vo, X. V. ve Tran, T. T. A. (2020). Modelling volatility spillovers from the US equity market to ASEAN stock markets. Pacific-Basin Finance Journal, 59, 101246. doi:10.1016/j.pacfin.2019.101246
  • Wu, F., Guan, Z. ve Myers, R. J. (2011). Volatility spillover effects and cross hedging in corn and crude oil futures. Journal of Futures Markets, 31(11), 1052- 1075. doi:10.1002/fut.20499
  • Wu, M. ve Zhu, Z. (2019). The volatility spillover effect between the ınternational crude oil futures price and China’s stock market-multivariate bekk-garch model based on wavelet multiresolution. International Journal of Financial Research, 10(4).
  • Yarovaya, L., Brzeszczyński, J. ve Lau, C. K. M. (2016). Intra- and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures. International Review of Financial Analysis, 43, 96-114. doi:10.1016/j.irfa.2015.09.004
  • Yılmaz, K. (2010). Return and volatility spillovers among the East Asian equity markets. Journal of Asian Economics, The Financial Crisis of 2008-09: Origins, Issues, and Prospects, 21(3), 304-313. doi:10.1016/j.asieco.2009.09.001
  • Yoon, S.-M., Al Mamun, M., Uddin, G. S. ve Kang, S. H. (2019). Network connectedness and net spillover between financial and commodity markets. The North American Journal of Economics and Finance, 48, 801-818. doi:10.1016/j.najef.2018.08.012
  • Zeng, T., Yang, M. ve Shen, Y. (2020). Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks. Economic Modelling, 90, 209-220. doi:10.1016/j.econmod.2020.05.003
  • Zhou, X., Zhang, W. ve Zhang, J. (2012). Volatility spillovers between the Chinese and world equity markets. Pacific-Basin Finance Journal, 20(2), 247-270. doi:10.1016/j.pacfin.2011.08.002