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Finansal Türbülans Dönemlerinde Gelişmekte Olan Hisse Senedi Piyasaları Arasında Dinamik Getiri Bağlantılılığı

Year 2024, Volume: 8 Issue: 2, 441 - 457, 31.05.2024
https://doi.org/10.29023/alanyaakademik.1314233

Abstract

Çalışmada; küresel finans krizi, COVID-19 pandemisi ve Rusya-Ukrayna savaşı gibi belirsizliğin arttığı dönemlerde gelişmekte olan ülke hisse senedi piyasaları arasındaki dinamik bağlantılılık ilişkileri araştırılmıştır. Gelişmekte olan yedi ülkenin (E7 ülkeleri: Çin, Hindistan, Brezilya, Meksika, Endonezya, Rusya ve Türkiye) finansal piyasalarını temsilen ülkelerin gösterge niteliğindeki hisse senedi piyasa endekslerinin 02.01.2006 ile 31.12.2022 dönemi günlük kapanış verileri kullanılarak Zamanla Değişen Parametreli VAR (TVP-VAR) modeli ile analiz gerçekleştirilmiştir. Analiz sonucunda Brezilya ve Meksika piyasalarının net şok yayıcısı; Çin, Hindistan, Endonezya, Rusya ve Türkiye piyasalarının ise net şok alıcısı olduğu belirlenmiştir. Ayrıca, küresel finans krizi, ABD’nin kredi notunun düşürülmesi, Çin borsa çöküşü ve COVID-19 pandemisi gibi küresel ekonomik faaliyetleri etkileyen olayların E7 ülkeleri arasındaki ortalama dinamik bağlantılılığı arttırdığı; yerel ölçekli ekonomik, siyasi ve sosyal olayların ise toplam risk düzeyi üzerinde anlamlı bir etkisinin olmadığı tespit edilmiştir. Bu durum, küresel ekonomide ve finansal piyasalarda ortaya çıkabilecek türbülans dönemlerinde E7 ülkeleri hisse senedi piyasası varlıklarından oluşan bir portföyün uluslararası portföy çeşitlendirmesinin sağlayacağı faydayı azaltacağını ortaya koymuştur.

References

  • Akyıldırım, E., Güneş, H., & Çelik, İ. (2022). Türkiye’de finansal varlıklar arasında dinamik bağlantılılık: TVP-VAR modelinden kanıtlar. Gazi İktisat ve İşletme Dergisi, 8(2), 346–363. https://doi.org/https://doi.org/10.30855/gjeb.2022.8.2.010
  • Alqaralleh, H., Awadallah, D., & Al-Ma’aitah, N. (2019). Dynamic asymmetric financial connectedness under tail dependence and rendered time variance: Selected evidence from emerging MENA stock markets. Borsa Istanbul Review, 19(4), 323–330. https://doi.org/10.1016/J.BIR.2019.06.001
  • Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4). https://doi.org/10.3390/jrfm13040084
  • Apostolakis, G.N., Floros, C., & Giannellis, N. (2022). On bank return and volatility spillovers: Identifying transmitters and receivers during crisis periods. International Review of Economics & Finance, 82, 156–176. https://doi.org/10.1016/J.IREF.2022.06.009
  • 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
  • Baruník, J., Kočenda, E., & Vácha, L. (2016). Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27, 55–78. https://doi.org/10.1016/J.FINMAR.2015.09.003
  • Benlagha, N., Karim, S., Naeem, M.A., Lucey, B.M., & Vigne, S.A. (2022). Risk connectedness between energy and stock markets: Evidence from oil importing and exporting countries. Energy Economics, 115, 106348. https://doi.org/10.1016/J.ENECO.2022.106348
  • Bossman, A., & Gubareva, M. (2023). Asymmetric impacts of geopolitical risk on stock markets: A comparative analysis of the E7 and G7 equities during the Russian-Ukrainian conflict. Heliyon, 9(2), e13626. https://doi.org/10.1016/J.HELIYON.2023.E13626
  • Bossman, A., Owusu Junior, P., & Tiwari, A.K. (2022). Dynamic connectedness and spillovers between Islamic and conventional stock markets: Time- and frequency-domain approach in COVID-19 era. Heliyon, 8(4), e09215. https://doi.org/10.1016/J.HELIYON.2022.E09215
  • Bouri, E., Gabauer, D., Gupta, R., & Tiwari, A.K. (2021). Volatility connectedness of major cryptocurrencies: The role of investor happiness. Journal of Behavioral and Experimental Finance, 30, 100463. https://doi.org/10.1016/J.JBEF.2021.100463
  • Chirilă, V. (2022). Risk and Financial Management Connectedness between Sectors: The Case of the Polish Stock Market before and during COVID-19. Journal of Risk and Financial Management, 15(8). https://doi.org/10.3390/jrfm15080322
  • Chowdhury, M.I.H., Balli, F., & Hassan, M.K. (2021). Network connectedness of World’s Islamic equity markets. Finance Research Letters, 41. https://doi.org/10.1016/J.FRL.2020.101878
  • Cui, J., Goh, M., Li, B., & Zou, H. (2021). Dynamic dependence and risk connectedness among oil and stock markets: New evidence from time-frequency domain perspectives. Energy, 216. https://doi.org/10.1016/J.ENERGY.2020.119302
  • Diebold, F.X., & Yılmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171.
  • Diebold, F.X., & Yılmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/J.IJFORECAST.2011.02.006
  • Diebold, F.X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/J.JECONOM.2014.04.012
  • Dong, Z., Li, Y., Zhuang, X., & Wang, J. (2022). Impacts of COVID-19 on global stock sectors: Evidence from time-varying connectedness and asymmetric nexus analysis. The North American Journal of Economics and Finance, 62, 101753. https://doi.org/10.1016/J.NAJEF.2022.101753
  • Ekinci, R., & Gençyürek, A.G. (2021). Dynamic connectedness between sector indices: Evidence from Borsa Istanbul. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 16(2), 512–534. https://doi.org/10.17153/oguiibf.879784
  • Engle, R.F., Gallo, G.M., & Velucchi, M. (2012). Volatility spillovers in East Asian financial markets: A MEM-based approach. The Review of Economics and Statistics, 94(1). http://www.jstor.org/stable/41349171
  • Gong, X. L., Liu, J. M., Xiong, X., & Zhang, W. (2022). Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network. International Review of Financial Analysis, 84, 102359. https://doi.org/10.1016/J.IRFA.2022.102359
  • Hammoudeh, S., Kang, S.H., Mensi, W., & Nguyen, D.K. (2016). Dynamic global linkages of the BRICS stock markets with the U.S. and Europe under external crisis shocks: Implications for portfolio risk forecasting. The World Economy, 39(11), 1703–1727. https://doi.org/https://doi.org/10.1111/twec.12433
  • Khalfaoui, R., Hammoudeh, S., & Rehman, M.Z. (2023). Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network. Emerging Markets Review, 54, 101002. https://doi.org/10.1016/J.EMEMAR.2023.101002
  • Koop, G., & Korobilis, D. (2013). Large time-varying parameter VARs. Journal of Econometrics, 177(2), 185–198. https://doi.org/10.1016/J.JECONOM.2013.04.007
  • Koop, G., & 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
  • Koop, G., Pesaran, M.H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4
  • Lahrech, A., & Sylwester, K. (2013). The impact of NAFTA on North American stock market linkages. The North American Journal of Economics and Finance, 25, 94–108. https://doi.org/10.1016/J.NAJEF.2013.04.001
  • Li, Z., Mo, B., & Nie, H. (2023). Time and frequency dynamic connectedness between cryptocurrencies and financial assets in China. International Review of Economics & Finance, 86, 46–57. https://doi.org/10.1016/J.IREF.2023.01.015
  • Mensi, W., Boubaker, F.Z., Al-Yahyaee, K.H., & Kang, S.H. (2018). Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets. Finance Research Letters, 25, 230–238. https://doi.org/10.1016/J.FRL.2017.10.032
  • Mensi, W., Hammoudeh, S., & Kang, S. H. (2017). Risk spillovers and portfolio management between developed and BRICS stock markets. The North American Journal of Economics and Finance, 41, 133–155. https://doi.org/10.1016/J.NAJEF.2017.03.006
  • Mensi, W., Shafiullah, M., Vo, X.V., & Kang, S. H. (2021). Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies. Resources Policy, 71, 102002. https://doi.org/10.1016/J.RESOURPOL.2021.102002
  • Moon, G.-H., & Yu, W.-C. (2010). Volatility spillovers between the US and the China stock markets: Structural break test with symmetric and asymmetric GARCH approaches. Global Economic Review, 39(2), 129–149.
  • Pesaran, H.H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
  • Şenol, Z., & Karaca, C. (2022). COVID-19 sürecinde borsalararası volatilite yayılımları: Kırılgan beşli ve gelişmiş ülke piyasaları örneği. Uluslararası Yönetim İktisat ve İşletme Dergisi, 18(2), 449–469. https://doi.org/10.17130/ijmeb.979135
  • Tiwari, A.K., Cunado, J., Gupta, R., & Wohar, M.E. (2018). Volatility spillovers across global asset classes: Evidence from time and frequency domains. The Quarterly Review of Economics and Finance, 70, 194–202. https://doi.org/10.1016/j.qref.2018.05.001
  • Wang, Y., & Guo, Z. (2018). The dynamic spillover between carbon and energy markets: New evidence. Energy, 149, 24–33. https://doi.org/10.1016/J.ENERGY.2018.01.145
  • Wu, F., Zhang, D., & Zhang, Z. (2019). Connectedness and risk spillovers in China’s stock market: A sectoral analysis. Economic Systems, 43(3–4), 100718. https://doi.org/10.1016/J.ECOSYS.2019.100718
  • Xu, H., & Hamori, S. (2012). Dynamic linkages of stock prices between the BRICs and the United States: Effects of the 2008–09 financial crisis. Journal of Asian Economics, 23(4), 344–352. https://doi.org/10.1016/j.asieco.2012.04.002
  • Zhang, D. (2017). Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Economics, 62, 323–333. https://doi.org/10.1016/j.eneco.2017.01.009

Dynamic Return Connectedness Among Emerging Equity Markets in Times of Financial Turbulence

Year 2024, Volume: 8 Issue: 2, 441 - 457, 31.05.2024
https://doi.org/10.29023/alanyaakademik.1314233

Abstract

In the study, the dynamic connectedness relations between the stock markets of developing countries were investigated during periods of increased uncertainty such as the global financial crisis, the COVID-19 pandemic, and the Russia-Ukraine war. Analysis was carried out with the Time Varying Parameter VAR (TVP-VAR) model, using the daily closing data of the indicative stock market indices of the countries representing the financial markets of seven emerging countries (E7 countries: China, India, Brazil, Mexico, Indonesia, Russia and Türkiye) for the period 02.01.2006 and 31.12.2022. As a result of the analysis, the net shock transmitter of the Brazil and Mexico markets; It was determined that the markets of China, India, Indonesia, Russia and Turkey were net shock receiver. In addition, events affecting global economic activities such as the global financial crisis, the downgrade of the US credit rating, the Chinese stock market crash and the COVID-19 pandemic increased the average dynamic connectedness among E7 countries; it has been determined that local scale economic, political and social events do not have a significant effect on the total risk level. This situation revealed that during periods of turbulence in the global economy and financial markets, a portfolio of E7 countries' equity market assets would reduce the benefits of international portfolio diversification.

References

  • Akyıldırım, E., Güneş, H., & Çelik, İ. (2022). Türkiye’de finansal varlıklar arasında dinamik bağlantılılık: TVP-VAR modelinden kanıtlar. Gazi İktisat ve İşletme Dergisi, 8(2), 346–363. https://doi.org/https://doi.org/10.30855/gjeb.2022.8.2.010
  • Alqaralleh, H., Awadallah, D., & Al-Ma’aitah, N. (2019). Dynamic asymmetric financial connectedness under tail dependence and rendered time variance: Selected evidence from emerging MENA stock markets. Borsa Istanbul Review, 19(4), 323–330. https://doi.org/10.1016/J.BIR.2019.06.001
  • Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4). https://doi.org/10.3390/jrfm13040084
  • Apostolakis, G.N., Floros, C., & Giannellis, N. (2022). On bank return and volatility spillovers: Identifying transmitters and receivers during crisis periods. International Review of Economics & Finance, 82, 156–176. https://doi.org/10.1016/J.IREF.2022.06.009
  • 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
  • Baruník, J., Kočenda, E., & Vácha, L. (2016). Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27, 55–78. https://doi.org/10.1016/J.FINMAR.2015.09.003
  • Benlagha, N., Karim, S., Naeem, M.A., Lucey, B.M., & Vigne, S.A. (2022). Risk connectedness between energy and stock markets: Evidence from oil importing and exporting countries. Energy Economics, 115, 106348. https://doi.org/10.1016/J.ENECO.2022.106348
  • Bossman, A., & Gubareva, M. (2023). Asymmetric impacts of geopolitical risk on stock markets: A comparative analysis of the E7 and G7 equities during the Russian-Ukrainian conflict. Heliyon, 9(2), e13626. https://doi.org/10.1016/J.HELIYON.2023.E13626
  • Bossman, A., Owusu Junior, P., & Tiwari, A.K. (2022). Dynamic connectedness and spillovers between Islamic and conventional stock markets: Time- and frequency-domain approach in COVID-19 era. Heliyon, 8(4), e09215. https://doi.org/10.1016/J.HELIYON.2022.E09215
  • Bouri, E., Gabauer, D., Gupta, R., & Tiwari, A.K. (2021). Volatility connectedness of major cryptocurrencies: The role of investor happiness. Journal of Behavioral and Experimental Finance, 30, 100463. https://doi.org/10.1016/J.JBEF.2021.100463
  • Chirilă, V. (2022). Risk and Financial Management Connectedness between Sectors: The Case of the Polish Stock Market before and during COVID-19. Journal of Risk and Financial Management, 15(8). https://doi.org/10.3390/jrfm15080322
  • Chowdhury, M.I.H., Balli, F., & Hassan, M.K. (2021). Network connectedness of World’s Islamic equity markets. Finance Research Letters, 41. https://doi.org/10.1016/J.FRL.2020.101878
  • Cui, J., Goh, M., Li, B., & Zou, H. (2021). Dynamic dependence and risk connectedness among oil and stock markets: New evidence from time-frequency domain perspectives. Energy, 216. https://doi.org/10.1016/J.ENERGY.2020.119302
  • Diebold, F.X., & Yılmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171.
  • Diebold, F.X., & Yılmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/J.IJFORECAST.2011.02.006
  • Diebold, F.X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/J.JECONOM.2014.04.012
  • Dong, Z., Li, Y., Zhuang, X., & Wang, J. (2022). Impacts of COVID-19 on global stock sectors: Evidence from time-varying connectedness and asymmetric nexus analysis. The North American Journal of Economics and Finance, 62, 101753. https://doi.org/10.1016/J.NAJEF.2022.101753
  • Ekinci, R., & Gençyürek, A.G. (2021). Dynamic connectedness between sector indices: Evidence from Borsa Istanbul. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 16(2), 512–534. https://doi.org/10.17153/oguiibf.879784
  • Engle, R.F., Gallo, G.M., & Velucchi, M. (2012). Volatility spillovers in East Asian financial markets: A MEM-based approach. The Review of Economics and Statistics, 94(1). http://www.jstor.org/stable/41349171
  • Gong, X. L., Liu, J. M., Xiong, X., & Zhang, W. (2022). Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network. International Review of Financial Analysis, 84, 102359. https://doi.org/10.1016/J.IRFA.2022.102359
  • Hammoudeh, S., Kang, S.H., Mensi, W., & Nguyen, D.K. (2016). Dynamic global linkages of the BRICS stock markets with the U.S. and Europe under external crisis shocks: Implications for portfolio risk forecasting. The World Economy, 39(11), 1703–1727. https://doi.org/https://doi.org/10.1111/twec.12433
  • Khalfaoui, R., Hammoudeh, S., & Rehman, M.Z. (2023). Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network. Emerging Markets Review, 54, 101002. https://doi.org/10.1016/J.EMEMAR.2023.101002
  • Koop, G., & Korobilis, D. (2013). Large time-varying parameter VARs. Journal of Econometrics, 177(2), 185–198. https://doi.org/10.1016/J.JECONOM.2013.04.007
  • Koop, G., & 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
  • Koop, G., Pesaran, M.H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4
  • Lahrech, A., & Sylwester, K. (2013). The impact of NAFTA on North American stock market linkages. The North American Journal of Economics and Finance, 25, 94–108. https://doi.org/10.1016/J.NAJEF.2013.04.001
  • Li, Z., Mo, B., & Nie, H. (2023). Time and frequency dynamic connectedness between cryptocurrencies and financial assets in China. International Review of Economics & Finance, 86, 46–57. https://doi.org/10.1016/J.IREF.2023.01.015
  • Mensi, W., Boubaker, F.Z., Al-Yahyaee, K.H., & Kang, S.H. (2018). Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets. Finance Research Letters, 25, 230–238. https://doi.org/10.1016/J.FRL.2017.10.032
  • Mensi, W., Hammoudeh, S., & Kang, S. H. (2017). Risk spillovers and portfolio management between developed and BRICS stock markets. The North American Journal of Economics and Finance, 41, 133–155. https://doi.org/10.1016/J.NAJEF.2017.03.006
  • Mensi, W., Shafiullah, M., Vo, X.V., & Kang, S. H. (2021). Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies. Resources Policy, 71, 102002. https://doi.org/10.1016/J.RESOURPOL.2021.102002
  • Moon, G.-H., & Yu, W.-C. (2010). Volatility spillovers between the US and the China stock markets: Structural break test with symmetric and asymmetric GARCH approaches. Global Economic Review, 39(2), 129–149.
  • Pesaran, H.H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
  • Şenol, Z., & Karaca, C. (2022). COVID-19 sürecinde borsalararası volatilite yayılımları: Kırılgan beşli ve gelişmiş ülke piyasaları örneği. Uluslararası Yönetim İktisat ve İşletme Dergisi, 18(2), 449–469. https://doi.org/10.17130/ijmeb.979135
  • Tiwari, A.K., Cunado, J., Gupta, R., & Wohar, M.E. (2018). Volatility spillovers across global asset classes: Evidence from time and frequency domains. The Quarterly Review of Economics and Finance, 70, 194–202. https://doi.org/10.1016/j.qref.2018.05.001
  • Wang, Y., & Guo, Z. (2018). The dynamic spillover between carbon and energy markets: New evidence. Energy, 149, 24–33. https://doi.org/10.1016/J.ENERGY.2018.01.145
  • Wu, F., Zhang, D., & Zhang, Z. (2019). Connectedness and risk spillovers in China’s stock market: A sectoral analysis. Economic Systems, 43(3–4), 100718. https://doi.org/10.1016/J.ECOSYS.2019.100718
  • Xu, H., & Hamori, S. (2012). Dynamic linkages of stock prices between the BRICs and the United States: Effects of the 2008–09 financial crisis. Journal of Asian Economics, 23(4), 344–352. https://doi.org/10.1016/j.asieco.2012.04.002
  • Zhang, D. (2017). Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Economics, 62, 323–333. https://doi.org/10.1016/j.eneco.2017.01.009
There are 38 citations in total.

Details

Primary Language Turkish
Subjects Time-Series Analysis, Financial Econometrics, Financial Markets and Institutions
Journal Section Makaleler
Authors

Ercüment Doğru 0000-0003-2650-9326

Publication Date May 31, 2024
Acceptance Date May 13, 2024
Published in Issue Year 2024 Volume: 8 Issue: 2

Cite

APA Doğru, E. (2024). Finansal Türbülans Dönemlerinde Gelişmekte Olan Hisse Senedi Piyasaları Arasında Dinamik Getiri Bağlantılılığı. Alanya Akademik Bakış, 8(2), 441-457. https://doi.org/10.29023/alanyaakademik.1314233