SavedAdd your journal

Journals / Quantrade Journal of Complex Systems in Social Sciences / 2025 / Cilt: 7 Sayı: 2

Dynamic Connectedness and Risk Transmission Among Global Sectoral Indices: Findings from COVID-19 and the Russia-Ukraine War

Küresel Sektörel Endeksler Arasındaki Dinamik Bağlantılılık ve Risk Aktarımı: COVID-19 ve Rusya-Ukrayna Savaşından Bulgular

Emrah Şahin(HİTİT ÜNİVERSİTESİ)0000-0002-1001-6511
Pages
154–172
Publication Date
2025-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal significant increases in cross-sectoral connectedness during both crises.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study explores the dynamic relationships and volatility spillovers among global sectoral indices during the Covid-19 pandemic and the Russia–Ukraine war.
📊
Key Findings & Data:The findings reveal significant increases in cross-sectoral connectedness during both crises.
💡
Conclusion & Impact:Furthermore, the results underscore the financial sector’s leading role in transmitting risk across sectors during both crisis periods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Covid-19 · Rusya Ukrayna Savaşı · MSCI ACWI

Abstract

This study explores the dynamic relationships and volatility spillovers among global sectoral indices during the Covid-19 pandemic and the Russia–Ukraine war. It evaluates changes in the connectedness of sectoral indices throughout both crisis periods. The finance, materials, industrials, health care, energy, and consumer staples sectors—represented by the MSCI ACWI indices—are analysed using the TVP-VAR-SV model. The findings reveal significant increases in cross-sectoral connectedness during both crises. During the pandemic, the health care sector was at the core of the connectedness network, exhibiting strong linkages with other sectors. Conversely, during the war, the energy sector assumed a central role, with intensified interactions particularly between the energy sector and the health care, financial, and industrials sectors. Marked increases in volatility were observed in the health care and financial sectors during the pandemic, and in the energy and materials sectors during the war. In the financial sector, short-term shocks predominated during the war, whereas in the energy and health care sectors, short-term shocks in the early phase gave way to long-term, persistent shocks later on. Over time, a changing impulse–response dynamic emerged: short-term effects dominated the health care and industrials sectors during the pandemic, while long-term impacts were more prevalent in the financial and consumer staples sectors. Furthermore, the results underscore the financial sector’s leading role in transmitting risk across sectors during both crisis periods.

Özet

Bu çalışmada Covid-19 pandemisi ve Rusya-Ukrayna savaşı döneminde küresel sektörel endeksler arasındaki dinamik ilişkiler ve oynaklık etkileşimleri araştırılmıştır. Her iki kriz döneminde sektörel endeksler arasındaki bağlantılılık ilişkisinde değişimler değerlendirilmiştir. MSCI ACWI endeksleri kapsamında yer alan finansal, malzeme, sanayi, sağlık, enerji, temel tüketim malları sektörleri ele alınarak TVP-VAR-SV modeli ile incelenmiştir. Sonuçlar hem pandemi hem de savaş döneminde küresel sektörel endeksler arasındaki bağlantılılıklarda önemli artışlar yaşandığını göstermektedir. Pandemi döneminde sağlık sektörü bağlantılılık yapısının merkezinde yer almış, diğer sektörler ile güçlü bağlantılılık ilişkisi geliştirmiştir. Savaş döneminde ise enerji sektörünün bağlantılılık ilişkisinin merkezinde yer almış; enerji sektörü ile sağlık, finansal ve sanayi sektörleri arasındaki etkileşim güçlenmiştir. Pandemi döneminde sağlık ve finans; savaş döneminde ise enerji ve malzeme sektörlerine yüksek volatilite artışları yaşanmıştır. Savaş döneminde finansal sektöründe kısa vadeli şoklar baskınken, enerji ve sağlık sektörlerinde savaşın ilk dönemlerinde kısa vadeli şoklar, ilerleyen dönemlerde yerini uzun vadeli kalıcı şoklara bırakmıştır. Zamanla değişen etki tepki analiz sonuçları, pandemi döneminde sağlık ve sanayi sektörlerinde kısa vadeli şokların, finansal ve temel tüketim malları sektörlerinde uzun vadeli etkilerin baskın olduğunu göstermektedir. Ayrıca bulgular, finansal sektörünün her iki kriz döneminde de risk aktarımındaki öncü rolünü vurgulamaktadır.

🔖

Cite This Article

Şahin, E. (2025). Dynamic Connectedness and Risk Transmission Among Global Sectoral Indices: Findings from COVID-19 and the Russia-Ukraine War. Quantrade Journal of Complex Systems in Social Sciences, Cilt: 7 Sayı: 2, 154–172. https://ajindex.com/articles/1536509

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (50)

  1. Aizenman, J., Lindahl, R., Stenvall, D., & Uddin, G. S. (2024). Geopolitical shocks and commodity market dynamics: New evidence from the Russia-Ukraine conflict. European Journal of Political Economy, 85, 102574. https://doi.org/10.1016/j.ejpoleco.2024.102574
  2. Akhtaruzzaman, M., Boubaker, S., & Sensoy, A. (2021). Financial contagion during COVID–19 crisis. Finance research letters, 38, 101604. https://doi.org/10.1016/j.frl.2020.101604
  3. Alam, M. K., Tabash, M. I., Billah, M., Kumar, S., & Anagreh, S. (2022). The impacts of the Russia–Ukraine invasion on global markets and commodities: A dynamic connectedness among G7 and BRIC markets. Journal of Risk and Financial Management, 15(8), 352. https://doi.org/10.3390/jrfm15080352
  4. Almansour, B. Y., Elkrghli, S., Gaytan, J. C. T., & Mohnot, R. (2023). Interconnectedness dynamic spillover among US, Russian, and Ukrainian equity indices during the COVID-19 pandemic and the Russian–Ukrainian war. Heliyon, 9(12).
  5. Anyikwa, I., & Phiri, A. (2023). Quantile connectedness amongst BRICS equity markets during the COVID-19 pandemic and Russia–Ukraine war. Cogent Economics & Finance, 11(2), 2251300. https://doi.org/10.1080/23322039.2023.2251300
  6. Baqaee, D., & Farhi, E. (2022). Supply and demand in disaggregated Keynesian economies with an application to the Covid-19 crisis. American Economic Review, 112(5), 1397-1436.
  7. Cavaglia, S., Brightman, C., & Aked, M. (2000). The increasing importance of industry factors. Financial Analysts Journal, 56(5), 41-54. https://doi.org/10.2469/faj.v56.n5.2389
  8. Cheng, T., Liu, J., Yao, W., & Zhao, A. B. (2022). The impact of COVID-19 pandemic on the volatility connectedness network of global stock market. Pacific-Basin Finance Journal, 71, 101678. https://doi.org/10.1016/j.pacfin.2021.101678
  9. Chirilă, V. (2022). Connectedness between sectors: the case of the polish stock market before and during COVID-19. Journal of Risk and Financial Management, 15(8), 322. https://doi.org/10.3390/jrfm15080322
  10. Choi, S. Y. (2023). The dynamic network of industries in US stock market: Evidence of GFC, COVID-19 pandemic and Russia-Ukraine war. Heliyon, 9(9). https://doi.org/10.1016/j.heliyon.2023.e19726
  11. Costa, A., Matos, P., & da Silva, C. (2022). Sectoral connectedness: New evidence from US stock market during COVID-19 pandemics. Finance Research Letters, 45, 102124. https://doi.org/10.1016/j.frl.2021.102124
  12. Countryman, A. M., Litvinov, V., Kolodiazhnyi, I., Bogonos, M., & Nivievskyi, O. (2025). Global economic effects of war‐induced agricultural export declines from Ukraine. Applied Economic Perspectives and Policy, 47(2), 624-665. https://doi.org/10.1002/aepp.13468
  13. del Rio-Chanona, R. M., Mealy, P., Pichler, A., Lafond, F., & Farmer, J. D. (2020). Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective. Oxford Review of Economic Policy, 36(Supplement_1), 94-137.
  14. Duncan, A. S., & Kabundi, A. (2013). Domestic and foreign sources of volatility spillover to South African asset classes. Economic Modelling, 31, 566-573. https://doi.org/10.1016/j.econmod.2012.11.016
  15. Ekinci, R., & Gençyürek, A. G. (2021). Dynamic connectedness between sector indices: Evidence from Borsa Istanbul. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 16(2), 512-534.
  16. Fernandez-Perez, A., Gilbert, A., Indriawan, I., & Nguyen, N. H. (2021). COVID-19 pandemic and stock market response: A culture effect. Journal of behavioral and experimental finance, 29, 100454.
  17. Fitriyah, F., & Mukminatin, Z. N. (2025). Return connectedness of sectoral stock indices on the Indonesian stock exchange during Russian-Ukraine conflict. Journal of Accounting and Investment, 26(1), 155-172. DOI: https://doi.org/10.18196/jai.v26i1.22406
  18. Frikha, W., Brahim, M., Jeribi, A., & Lahiani, A. (2023). COVID-19, Russia-Ukraine war and interconnectedness between stock and crypto markets: a wavelet-based analysis. Journal of Business Analytics, 6(4), 255-275. https://doi.org/10.1080/2573234X.2023.2193224
  19. Ghorbel, A., Loukil, S., & Bahloul, W. (2024). Connectedness between cryptocurrencies, gold and stock markets in the presence of the COVID-19 pandemic. European Journal of Management and Business Economics, 33(4), 466-487. https://doi.org/10.1108/EJMBE-10-2021-0281
  20. Gupta, R., & Basu, P. K. (2011). Does sector diversification benefit all markets?-analysis of Australian and Indian markets. The Asia Pacific Journal of Economics & Business, 15(1), 15.).
  21. Ha, LT (2023). TVP-VAR genişletilmiş ortak bağlantılı yaklaşımını uygulayarak COVID-19 pandemisi sırasında kripto para ve borsa piyasalarının birbirine bağlanması. Ekonomi Çalışmaları Dergisi, 50 (3), 407-428. https://doi.org/10.1108/JES-01-2022-0055
  22. İmre, S. (2021). Covid-19 pandemisinin seçili BİST sektör endeksleri üzerindeki etkisi. Journal of Management and Economics Research, 19(4), 335-348. http://dx.doi.org/10.11611/yead.984604
  23. Jebabli, I., Arouri, M., & Teulon, F. (2014). On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility. Energy Economics, 45, 66-98. https://doi.org/10.1016/j.eneco.2014.06.008
  24. Jiang, W., & Chen, Y. (2024). Impact of Russia-Ukraine conflict on the time-frequency and quantile connectedness between energy, metal and agricultural markets. Resources Policy, 88, 104376. https://doi.org/10.1016/j.resourpol.2023.104376
  25. Khan, S., Rehman, M. Z., Shahzad, M. R., Khan, N. U., & Abdul Razak, L. (2025). How prone are emerging markets' sectoral indices to global uncertainties? Evidence from the quantile connectedness approach with portfolio implications. International Journal of Emerging Markets, 20(4), 1569-1592. https://doi.org/10.3390/su142315908
  26. Laborda, R., & Olmo, J. (2021). Volatility spillover between economic sectors in financial crisis prediction: Evidence spanning the great financial crisis and Covid-19 pandemic. Research in International Business and Finance, 57, 101402. https://doi.org/10.1016/j.ribaf.2021.101402
  27. Leone, M., Manelli, A., & Pace, R. (2025). The Russia–Ukraine conflict and stock markets: Risk and spillovers. Risks, 13(7), 130. https://doi.org/10.3390/risks13070130
  28. Liu, Y., Wei, Y., Wang, Q., & Liu, Y. (2022). International stock market risk contagion during the COVID-19 pandemic. Finance Research Letters, 45, 102145. https://doi.org/10.1016/j.frl.2021.102145
  29. Mahran, H. A. (2023). The impact of the Russia–Ukraine conflict (2022) on volatility connectedness between the Egyptian stock market sectors: evidence from the DCC-GARCH-CONNECTEDNESS approach. The Journal of Risk Finance, 24(1), 105-121. https://doi.org/10.1108/JRF-06-2022-0163
  30. Maurya, P. K., Bansal, R., & Mishra, A. K. (2024). Dynamic connectedness among market volatilities: a perspective of COVID-19 and Russia-Ukraine conflict. Studies in Economics and Finance, 41(5), 1119-1140. https://doi.org/10.1108/SEF-01-2024-0029
  31. Mezghani, T., Ben Hamadou, F., & Boujelbène Abbes, M. (2021). The dynamic network connectedness and hedging strategies across stock markets and commodities: COVID-19 pandemic effect. Asia-Pacific Journal of Business Administration, 13(4), 520-552. https://doi.org/10.1108/APJBA-01-2021-0036
  32. Nakajima, J. (2011). Time-varying parameter VAR model with stochastic volatility: An overview of methodology and empirical applications.
  33. Noorani, G. M., Khan, M. T. A., & Khan, H. U. (2024). The Global Economic Consequences of the Russia-Ukraine War: Implications for Energy, Food Security, Post-COVID Recovery, and Regional Economic Stability. Policy Res. J, 2, 380-390.
  34. Obi, P., Waweru, F., & Nyangu, M. (2023). An event study on the reaction of equity and commodity markets to the onset of the Russia–Ukraine conflict. Journal of Risk and Financial Management, 16(5), 256. https://doi.org/10.3390/jrfm16050256
  35. Pichler, A., Pangallo, M., del Rio-Chanona, R. M., Lafond, F., & Farmer, J. D. (2021). In and out of lockdown: Propagation of supply and demand shocks in a dynamic input-output model. arXiv preprint arXiv:2102.09608.
  36. Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of economic studies, 72(3), 821-852. https://doi.org/10.1111/j.1467-937X.2005.00353.x
  37. Pruchnicka-Grabias, I., & Żebrowska-Suchodolska, D. (2024). Inter-Connectedness between Developed and Visegrad Stock Markets during Covid-19 Pandemic and Russia-Ukraine War. Torun International Studies, 1(19), 65-90.
  38. Qiao, S., Dang, Y. J., Ren, Z. Y., & Zhang, K. Q. (2023). The dynamic spillovers among carbon, fossil energy and electricity markets based on a TVP-VAR-SV method. Energy, 266, 126344. https://doi.org/10.1016/j.energy.2022.126344
  39. Qureshi, F. (2022). COVID-19 pandemic, economic indicators and sectoral returns: evidence from US and China, Economic Research-Ekonomska Istraživanja, 35(1), 2142-2172, https://doi.org/10.1080/1331677X.2021.1934508
  40. Rehman, M. U., Ahmad, N., Shahzad, S. J. H., & Vo, X. V. (2022). Dependence dynamics of stock markets during COVID-19. Emerging Markets Review, 51, 100894. https://doi.org/10.1016/j.ememar.2022.100894
  41. Rose, A., Chen, Z., & Wei, D. (2023). The economic impacts of Russia–Ukraine War export disruptions of grain commodities. Applied Economic Perspectives and Policy, 45(2), 645-665. https://doi.org/10.1002/aepp.13351
  42. Roy, S. ve Das, D. (2022). Economic impact of Covid-19 pandemic. A preprint, 1, 29. https://www.researchgate.net/publication/343222400_ECONOMIC_IMPACT_OF_COVID-19_PANDEMIC/citations#fullTextFileContent
  43. Samitas, A., Kampouris, E., & Polyzos, S. (2022). Covid-19 pandemic and spillover effects in stock markets: A financial network approach. International Review of Financial Analysis, 80, 102005. https://doi.org/10.1016/j.irfa.2021.102005
  44. Shahzad, S. J. H., Naeem, M. A., Peng, Z., & Bouri, E. (2021). Asymmetric volatility spillover among Chinese sectors during COVID-19. International Review of Financial Analysis, 75, 101754. https://doi.org/10.1016/j.irfa.2021.101754
  45. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, 1-48. https://doi.org/10.2307/1912017
  46. Toparlı, E. A., Çatık, A. N., & Balcılar, M. (2019). The impact of oil prices on the stock returns in Turkey: A TVP-VAR approach. Physica A: Statistical Mechanics and its Applications, 535, 122392.https://doi.org/10.1016/j.physa.2019.122392
  47. Uddin, G. S., Yahya, M., Goswami, G. G., Lucey, B., & Ahmed, A. (2022). Stock market contagion during the COVID-19 pandemic in emerging economies. International review of economics & finance, 79, 302-309. https://doi.org/10.1016/j.iref.2022.02.028
  48. Ullah, M., Sohag, K., Khan, S., & Sohail, H. M. (2023). Impact of Russia–Ukraine conflict on Russian financial market: Evidence from TVP-VAR and quantile-VAR analysis. Russian Journal of Economics, 9, 284–305. DOI 10.32609/j.ruje.9.105833
  49. Zeren, F., & Hızarcı, A. (2020). The impact of COVID-19 coronavirus on stock markets: evidence from selected countries. Muhasebe ve finans incelemeleri dergisi, 3(1), 78-84. https://doi.org/10.32951/mufider.706159
  50. Zhang, W., Sun, L., & Nguyen‐Van, P. (2025). Dynamic Relationship Between Global Economic Policy Uncertainty, Food Prices, and Maritime Transport: Evidence From the TVP‐VAR‐SV Model. Agribusiness. https://doi.org/10.1002/agr.70005