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Borsa İstanbul Hisse Senetleri ve Belirsizlik Endeksleri Arasındaki Dinamik Bağlantılılığının Değerlendirilmesi: QVAR Analizi

Year 2025, Volume: 26 Issue: 4, 740 - 754

Tuba Gülcemal

https://doi.org/10.37880/cumuiibf.1749548

Abstract

Bu çalışma, Borsa İstanbul 100 (BIST100) endeksi ile küresel belirsizlik göstergeleri olan Volatilite Endeksi (VIX), Küresel Ekonomi Politika Belirsizlik Endeksi (GEPU), Dünya Belirsizlik Endeksi (WUI) ve Jeopolitik Risk Endeksinin (GPR) 01/02/2008 ile 02/12/2024 dönemine ait aylık verisini kullanarak aralarındaki dinamik bağlantılılıkları ve şok iletimi mekanizmalarını R programında uygulanan Kantil Vektör Otoregresif (QVAR) modeliyle analiz etmeyi amaçlamaktadır. Bu yöntem düşen, normal ve yükselen olmak üzere farklı piyasa koşullarında değişen ilişkileri inceleme imkânı sunmaktadır.
Elde edilen bulgular, BIST100 endeksinin genel olarak net şok alıcısı konumunda olduğunu ve özellikle VIX ve GPR kaynaklı şoklara duyarlı olduğunu ortaya koymaktadır. Düşen piyasa koşullarında BIST100, GEPU ve GPR’den gelen şoklara karşı daha savunmasızken, normal koşullarda VIX’in etkisi artmaktadır. Yükselen piyasa koşullarında ise GPR ve VIX kaynaklı şoklar daha belirgindir. Dinamik toplam bağlantılılık düzeyinin kriz dönemlerinde önemli ölçüde yükseldiği ve COVID-19 gibi olağanüstü durumların bu artışı daha da pekiştirdiği görülmektedir.
Bulgular ayrıca, belirsizlik endekslerinin zamanla değişen şok yayıcı ya da alıcı roller üstlendiğini göstermektedir. Bu durum, piyasa rejimlerinin ve küresel gelişmelerin finansal etkileşimler üzerindeki önemini vurgulamaktadır. Çalışma, yatırımcılar ve politika yapıcılar için risk yönetimi ve stratejik karar alma süreçlerinde belirsizlik göstergelerinin dikkate alınmasının önemini ortaya koymaktadır.

Keywords

BIST100 Belirsizlik Endeksleri QVAR Dinamik Bağlantılılık Şok İletimi

References

  • Aboura, S., & Chevallier, J. (2015). Volatility returns with vengeance: financial markets vs. commodities. Research in International Business and Finance, 33, 334–354.
  • Ahir, H., Bloom, N., & Furceri, D. (2018). The world uncertainty index. Stanford Institute for Economic Policy Research Working Paper.
  • Albulescu, C. T., Demirer, R., Raheem, I. D., & Tiwari, A. K. (2019). Does the U.S. economic policy uncertainty connect financial markets? Evidence from oil and commodity currencies. Energy Economics, 83, 375–388.
  • Altinkeski, B. K., Dibooglu, S., Cevik, E. I., Kilic, Y., & Bugan, M. F. (2024). Quantile connectedness between VIX and global stock markets. Borsa Istanbul Review, 24, 71-79. https://doi.org/10.1016/j.bir.2024.07.006
  • Ando, T., Greenwood-Nimmo, M. & Shin, Y. (2018). Quantile connectedness: Modelling tail behaviour in the topology of financial networks. SSRN. doi:https://dx.doi.org/10.2139/ssrn.3164772
  • Arouri, M., Estay, C., Rault, C., & Roubaud, D. (2016). Economic policy uncertainty and stock markets: Long-run evidence from the US. Finance Research Letters, 18, 136-141.
  • Baker, S.R., Bloom, N. & Davis, S.J. (2013). Measuring economic policy uncertainty. Stanford University Working paper
  • Baker, S. R., Nicholas B., Steven J. D. & Kost, K. (2019). Policy news and stock market volatility. NBER Working Paper, 1050. https://www.nber.org/papers/w25720.pdf
  • Balcilar, M., Chang, T., Gupta, R., & Li, X. (2013). The causal relationship between economic policy uncertainty and stock returns in China and India: Evidence from A Bootstrap Rolling-Window Approach. University of Pretoria Working paper.
  • Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685.
  • Caldara, D. & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review. 112(4), 1194-1225.
  • Chatziantoniou, I., Gabauer, D. & Stenfors, A. (2021). Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach. Econ. Lett. 204, 109891 https://doi.org/10.1016/j.econlet.2021.109891
  • Chen, B. X., & Sun, Y. L. (2022). The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods. The North American Journal of Economics and Finance, 63, 101831.
  • Chen, X., Yao, Y., Wang, L. & Huang, S. (2024). How EPU, VIX, and GPR interact with the dynamic connectedness among commodity and financial markets: Evidence from wavelet analysis. The North American Journal of Economics and Finance, 74, 102217.
  • Chen, X. (2023). Are the shocks of EPU, VIX, and GPR indexes on the oil-stock nexus alike? A time-frequency analysis. Applied Economics, 55, 5637–5652.
  • Dai, Z., Zhang, X., & Liang, C. (2024). Efficient predictability of oil price: The role of VIX-based panic index shadow line difference. Energy Economics, 129, 107234.
  • Ding, L., Huang, Y., & Pu, X. (2014). Volatility linkage across global equity markets. Global Finance Journal, 25, 71–89.
  • Finta, M. A. (2023). Higher-order risk premium and return spillovers between commodity and stock markets. SSRN Electronic Journal, 28, 100358.
  • Gao, J., Sheng Z., Niall O’Sullivan & Sherman, M. (2019). The role of economic uncertainty in UK stock returns. Journal of Risk and Financial Management, 12(5): 1-16.
  • Gabauer, D. (2021). Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management,60, 100680.
  • Gozgor, G., Lau, C. K. M. & Bilgin, M. H. (2016). Commodity markets volatility transmission: Roles of risk perceptions and uncertainty in financial markets. Journal of International Financial Markets, Institutions and Money, 44, 35-45.
  • Gürbüz, A. (2024). Heterojen belirsizlik endekslerinin BIST 100 endeksi üzerindeki etkisi: Bir ardl sınır testi yaklaşımı. Finans Politik & Ekonomik Yorumlar, 61(670), 29-51.
  • İltaş, Y. & Güzel, F. (2021). Borsa endeksi ve belirsizlik göstergeleri arasındaki nedensellik ilişkisi: Türkiye örneğİ. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 39(3), 411-424.
  • Jiang, W., Dong, L., & Chen, Y. (2023). Time-frequency connectedness among traditional/new energy, green finance, and ESG in pre-and post-Russia-Ukraine war periods. Resources Policy, 83, 103618.
  • Kayani, U., Sheikh, U. A., Khalfaoui, R., Roubaud, D. & Hammoudeh, S. (2024). Impact of climate policy uncertainty (cpu) and global energy uncertainty (EU) news on US sectors: The moderating role of CPU on the EU and US sectoral stock nexus. Journal of Environmental Management, 366, 121654.
  • Khan, N., Saleem, A. & Ozkan, O. (2023). Do geopolitical oil price risk influence stock market returns and volatility of Pakistan: Evidence from novel non-parametric quantile causality approach. Resources Policy, 81, 103355.
  • Koop, G., Pesaran, M.H. & Potter, S.M. (1996). Impulse response analysis in nonlinear multivariate models. J. Econometrics, 74(1), 119–147.
  • Korkmaz, Ö. & Güngör, S. (2018). Küresel ekonomi politika belirsizliğinin Borsa İstanbul’da işlem gören seçilmiş endeks getirileri üzerindeki etkisi. Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 6: 211-219
  • Li, Y., Huang, J., & Chen, J. (2021). Dynamic spillovers of geopolitical risks and gold prices: New evidence from 18 emerging economies. Resources Policy, 70, Article 101938.
  • Li, X., Li, B., Wei, G., Bai, L., Wei, Y., & Liang, C. (2021). Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US. Resources Policy, 73, Article 102166.
  • Liu, L. & Zhang, T. (2015). Economic policy uncertainty and stock market volatility. Finance Research Letter, 15, 99–105.
  • Mensi, W., Rehman, M. U., Maitra, D., Al-Yahyaee, K. H., & Vo, X. V. (2021). Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain. Resources Policy, 72, Article 102062.
  • Mo, B., Nie, H., & Zhao, R. (2024). Dynamic nonlinear effects of geopolitical risks on commodities: Fresh evidence from quantile methods. Energy, 288, 129759.
  • Pastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance, 67(4), 1219-1264.
  • Ren, X., An, Y., & Jin, C. (2023). The asymmetric effect of geopolitical risk on China’s crude oil prices: New evidence from a QARDL approach. Finance Research Letters, 53, Article 103637
  • Sadeghzadeh, E. & H. Aksu, L.E. (2020). Borsa İstanbul ve belirsizlik endeksi arasındaki ilişkilerin doğrusal olup olmadığına dair incelemeler (1998:01-2018:12). Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 24 (1), 429-446.
  • Shah, A. A., & Dar, A. B. (2022). Asymmetric, time and frequency-based spillover transmission in financial and commodity markets. The Journal of Economic Asymmetries, 25, e00241
  • Shahzad, S. J. H., Raza, N., Balcilar, M., Ali, S., & Shahbaz, M. (2017). Can economic policy uncertainty and investors sentiment predict commodities returns and volatility? Resources Policy, 53, 208–218.
  • Smales, Lee A. (2022). Spreading the fear: The central role of CBOE VIX in global stock market uncertainty. Global Finance Journal, Elsevier, 51(C).
  • Umar, Z., Bossman, A., Choi, S.-Y., & Teplova, T. (2022). Does geopolitical risk matter for global asset returns? Evidence from quantile-on-quantile regression. Finance Research Letters, 48,102991
  • Xiao, J., & Liu, H. (2023). The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter? Resources Policy, 82, 103533
  • Yin, H., Chang, L. & Wang, S. (2023). The impact of China’s economic uncertainty on commodity and financial markets. Resources Policy, 84, Article 103779.
  • Zheng, J., Wen, B., Jiang, Y., Wang, X., & Shen, Y. (2023). Risk spillovers across geopolitical risk and global financial markets. Energy Economics, 127, 107051.
  • Zheng, D., Zhao, C., & Hu, J. (2023). Impact of geopolitical risk on the volatility of natural resource commodity futures prices in China. Resources Policy, 83, 103568.
  • Zhu, H., Huang, R., Wang, N., & Hau, L. (2019). Does economic policy uncertainty matter for commodity market in China? Evidence from quantile regression. Applied Economics, 52, 2292–2308.

Evaluating the Dynamic Connectedness of Borsa Istanbul Stocks and Uncertainty Indices: A QVAR Analysis

Year 2025, Volume: 26 Issue: 4, 740 - 754

Tuba Gülcemal

https://doi.org/10.37880/cumuiibf.1749548

Abstract

This study aims to analyze the dynamic connectedness and shock transmission mechanisms between the Borsa Istanbul 100 (BIST100) index and global uncertainty indicators, including the Volatility Index (VIX), Global Economic Policy Uncertainty Index (GEPU), World Uncertainty Index (WUI), and Geopolitical Risk Index (GPR), using the Quantile Vector Autoregression (QVAR) model at R program based on monthly data covering the period from February 1, 2008, to December 2, 2024. This approach enables the examination of relationships under different market regimes—bearish, normal, and bullish—offering a comprehensive perspective on time-varying interactions.
Empirical findings indicate that the BIST100 index primarily acts as a net shock receiver, with a particularly high sensitivity to shocks originating from VIX and GPR. During bearish conditions, BIST100 is more vulnerable to shocks from GEPU and GPR, while under normal conditions, VIX plays a dominant role. In bullish regimes, the impacts of GPR and VIX become more pronounced. Total dynamic connectedness increases significantly during periods of heightened uncertainty and financial stress, especially during events such as the COVID-19 pandemic.
The study also finds that uncertainty indices alternate between being net transmitters or receivers of shocks depending on the market regime. This highlights the critical role of global developments and market states in shaping financial interdependencies. The findings emphasize the importance of incorporating uncertainty indicators into portfolio risk management and policy frameworks. By understanding these dynamics, investors and policymakers can develop more effective strategies to mitigate risk and respond proactively to external shocks in emerging markets.

Keywords

BIST100 Uncertainty Indices QVAR Dynamic Connectedness Shock Transmission

References

  • Aboura, S., & Chevallier, J. (2015). Volatility returns with vengeance: financial markets vs. commodities. Research in International Business and Finance, 33, 334–354.
  • Ahir, H., Bloom, N., & Furceri, D. (2018). The world uncertainty index. Stanford Institute for Economic Policy Research Working Paper.
  • Albulescu, C. T., Demirer, R., Raheem, I. D., & Tiwari, A. K. (2019). Does the U.S. economic policy uncertainty connect financial markets? Evidence from oil and commodity currencies. Energy Economics, 83, 375–388.
  • Altinkeski, B. K., Dibooglu, S., Cevik, E. I., Kilic, Y., & Bugan, M. F. (2024). Quantile connectedness between VIX and global stock markets. Borsa Istanbul Review, 24, 71-79. https://doi.org/10.1016/j.bir.2024.07.006
  • Ando, T., Greenwood-Nimmo, M. & Shin, Y. (2018). Quantile connectedness: Modelling tail behaviour in the topology of financial networks. SSRN. doi:https://dx.doi.org/10.2139/ssrn.3164772
  • Arouri, M., Estay, C., Rault, C., & Roubaud, D. (2016). Economic policy uncertainty and stock markets: Long-run evidence from the US. Finance Research Letters, 18, 136-141.
  • Baker, S.R., Bloom, N. & Davis, S.J. (2013). Measuring economic policy uncertainty. Stanford University Working paper
  • Baker, S. R., Nicholas B., Steven J. D. & Kost, K. (2019). Policy news and stock market volatility. NBER Working Paper, 1050. https://www.nber.org/papers/w25720.pdf
  • Balcilar, M., Chang, T., Gupta, R., & Li, X. (2013). The causal relationship between economic policy uncertainty and stock returns in China and India: Evidence from A Bootstrap Rolling-Window Approach. University of Pretoria Working paper.
  • Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685.
  • Caldara, D. & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review. 112(4), 1194-1225.
  • Chatziantoniou, I., Gabauer, D. & Stenfors, A. (2021). Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach. Econ. Lett. 204, 109891 https://doi.org/10.1016/j.econlet.2021.109891
  • Chen, B. X., & Sun, Y. L. (2022). The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods. The North American Journal of Economics and Finance, 63, 101831.
  • Chen, X., Yao, Y., Wang, L. & Huang, S. (2024). How EPU, VIX, and GPR interact with the dynamic connectedness among commodity and financial markets: Evidence from wavelet analysis. The North American Journal of Economics and Finance, 74, 102217.
  • Chen, X. (2023). Are the shocks of EPU, VIX, and GPR indexes on the oil-stock nexus alike? A time-frequency analysis. Applied Economics, 55, 5637–5652.
  • Dai, Z., Zhang, X., & Liang, C. (2024). Efficient predictability of oil price: The role of VIX-based panic index shadow line difference. Energy Economics, 129, 107234.
  • Ding, L., Huang, Y., & Pu, X. (2014). Volatility linkage across global equity markets. Global Finance Journal, 25, 71–89.
  • Finta, M. A. (2023). Higher-order risk premium and return spillovers between commodity and stock markets. SSRN Electronic Journal, 28, 100358.
  • Gao, J., Sheng Z., Niall O’Sullivan & Sherman, M. (2019). The role of economic uncertainty in UK stock returns. Journal of Risk and Financial Management, 12(5): 1-16.
  • Gabauer, D. (2021). Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management,60, 100680.
  • Gozgor, G., Lau, C. K. M. & Bilgin, M. H. (2016). Commodity markets volatility transmission: Roles of risk perceptions and uncertainty in financial markets. Journal of International Financial Markets, Institutions and Money, 44, 35-45.
  • Gürbüz, A. (2024). Heterojen belirsizlik endekslerinin BIST 100 endeksi üzerindeki etkisi: Bir ardl sınır testi yaklaşımı. Finans Politik & Ekonomik Yorumlar, 61(670), 29-51.
  • İltaş, Y. & Güzel, F. (2021). Borsa endeksi ve belirsizlik göstergeleri arasındaki nedensellik ilişkisi: Türkiye örneğİ. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 39(3), 411-424.
  • Jiang, W., Dong, L., & Chen, Y. (2023). Time-frequency connectedness among traditional/new energy, green finance, and ESG in pre-and post-Russia-Ukraine war periods. Resources Policy, 83, 103618.
  • Kayani, U., Sheikh, U. A., Khalfaoui, R., Roubaud, D. & Hammoudeh, S. (2024). Impact of climate policy uncertainty (cpu) and global energy uncertainty (EU) news on US sectors: The moderating role of CPU on the EU and US sectoral stock nexus. Journal of Environmental Management, 366, 121654.
  • Khan, N., Saleem, A. & Ozkan, O. (2023). Do geopolitical oil price risk influence stock market returns and volatility of Pakistan: Evidence from novel non-parametric quantile causality approach. Resources Policy, 81, 103355.
  • Koop, G., Pesaran, M.H. & Potter, S.M. (1996). Impulse response analysis in nonlinear multivariate models. J. Econometrics, 74(1), 119–147.
  • Korkmaz, Ö. & Güngör, S. (2018). Küresel ekonomi politika belirsizliğinin Borsa İstanbul’da işlem gören seçilmiş endeks getirileri üzerindeki etkisi. Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 6: 211-219
  • Li, Y., Huang, J., & Chen, J. (2021). Dynamic spillovers of geopolitical risks and gold prices: New evidence from 18 emerging economies. Resources Policy, 70, Article 101938.
  • Li, X., Li, B., Wei, G., Bai, L., Wei, Y., & Liang, C. (2021). Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US. Resources Policy, 73, Article 102166.
  • Liu, L. & Zhang, T. (2015). Economic policy uncertainty and stock market volatility. Finance Research Letter, 15, 99–105.
  • Mensi, W., Rehman, M. U., Maitra, D., Al-Yahyaee, K. H., & Vo, X. V. (2021). Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain. Resources Policy, 72, Article 102062.
  • Mo, B., Nie, H., & Zhao, R. (2024). Dynamic nonlinear effects of geopolitical risks on commodities: Fresh evidence from quantile methods. Energy, 288, 129759.
  • Pastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance, 67(4), 1219-1264.
  • Ren, X., An, Y., & Jin, C. (2023). The asymmetric effect of geopolitical risk on China’s crude oil prices: New evidence from a QARDL approach. Finance Research Letters, 53, Article 103637
  • Sadeghzadeh, E. & H. Aksu, L.E. (2020). Borsa İstanbul ve belirsizlik endeksi arasındaki ilişkilerin doğrusal olup olmadığına dair incelemeler (1998:01-2018:12). Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 24 (1), 429-446.
  • Shah, A. A., & Dar, A. B. (2022). Asymmetric, time and frequency-based spillover transmission in financial and commodity markets. The Journal of Economic Asymmetries, 25, e00241
  • Shahzad, S. J. H., Raza, N., Balcilar, M., Ali, S., & Shahbaz, M. (2017). Can economic policy uncertainty and investors sentiment predict commodities returns and volatility? Resources Policy, 53, 208–218.
  • Smales, Lee A. (2022). Spreading the fear: The central role of CBOE VIX in global stock market uncertainty. Global Finance Journal, Elsevier, 51(C).
  • Umar, Z., Bossman, A., Choi, S.-Y., & Teplova, T. (2022). Does geopolitical risk matter for global asset returns? Evidence from quantile-on-quantile regression. Finance Research Letters, 48,102991
  • Xiao, J., & Liu, H. (2023). The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter? Resources Policy, 82, 103533
  • Yin, H., Chang, L. & Wang, S. (2023). The impact of China’s economic uncertainty on commodity and financial markets. Resources Policy, 84, Article 103779.
  • Zheng, J., Wen, B., Jiang, Y., Wang, X., & Shen, Y. (2023). Risk spillovers across geopolitical risk and global financial markets. Energy Economics, 127, 107051.
  • Zheng, D., Zhao, C., & Hu, J. (2023). Impact of geopolitical risk on the volatility of natural resource commodity futures prices in China. Resources Policy, 83, 103568.
  • Zhu, H., Huang, R., Wang, N., & Hau, L. (2019). Does economic policy uncertainty matter for commodity market in China? Evidence from quantile regression. Applied Economics, 52, 2292–2308.
There are 45 citations in total.

Details

Primary Language Turkish
Subjects Finance
Journal Section Makaleler
Authors

Tuba Gülcemal 0000-0003-4806-8568

Early Pub Date October 22, 2025
Publication Date October 27, 2025
Submission Date July 23, 2025
Acceptance Date October 19, 2025
Published in Issue Year 2025 Volume: 26 Issue: 4

Cite

APA Gülcemal, T. (2025). Borsa İstanbul Hisse Senetleri ve Belirsizlik Endeksleri Arasındaki Dinamik Bağlantılılığının Değerlendirilmesi: QVAR Analizi. Cumhuriyet Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 26(4), 740-754. https://doi.org/10.37880/cumuiibf.1749548
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