Evaluating Dynamic Connectedness Between Economic Sanctions Sentiment, Uncertainty Factors, and Financial Assets: A Quantile VAR Approach
DOI:
https://doi.org/10.47743/saeb-2025-0037Keywords:
connectedness, economic sanctions, investor sentiment, Urals oil, RussiaCoin Bitcoin, QVAR.Abstract
This paper investigates the dynamic connection between investor sentiment and a range of asset classes during the Russia-Ukraine conflict. Using daily data from January 1, 2022, to April 20, 2023, we employ the Quantile Vector Autoregressive (QVAR) connectedness framework to examine the connectedness of investor sentiment, financial stress, geopolitical risk, on commodities, fiat currencies, and stock markets. Our results reveal a time-varying and quantile-dependent pattern of connectedness, with RUWESsent consistently emerging as the primary net transmitter of shocks across all quantiles. Furthermore, the net directional connectedness highlights persistent and robust spillovers between RUWESsent, the Financial Stress Index (FSI), the Geopolitical Risk Index (GPR), and key financial assets throughout much of the sample period. This underscores a high degree of connectedness between sentiment-driven uncertainty and asset price dynamics. These findings provide valuable insights for investors, portfolio managers, regulators, and policymakers, emphasizing the importance of monitoring sentiment and geopolitical developments when formulating financial strategies during periods of heightened uncertainty.
References
Aalto, P., & Forsberg, T. (2016). The Structuration of Russia’s Geo-Economy under Economic Sanctions. Asia Europe Journal, 14(2), 221-237. http://dx.doi.org/10.1007/s10308-015-0446-6
Abakah, E. J. A., Abdullah, M., Yousaf, I., Kumar Tiwari, A., & Li, Y. (2024). Economic Sanctions Sentiment and Global Stock Markets. Journal of International Financial Markets, Institutions and Money, 91(March), 1-23. http://dx.doi.org/10.1016/j.intfin.2023.101910
Abakah, E. J. A., Adeabah, D., Tiwari, A. K., & Abdullah, M. (2023). Effect of Russia–Ukraine War Sentiment on Blockchain and FinTech Stocks. International Review of Financial Analysis, 90(November), 1-22. http://dx.doi.org/10.1016/j.irfa.2023.102948
Abid, I., Bouri, E., Galariotis, E., Guesmi, K., & Mzoughi, H. (2023). Bitcoin vs. Fiat Currencies : Insights from Extreme Dependence and Risk Spillover Analysis with Financial Markets. International Review of Financial Analysis, 90(3), 1-31. http://dx.doi.org/10.1016/j.irfa.2023.102806
Ahmed, S., Hasan, M. M., & Kamal, M. R. (2023). Russia–Ukraine Crisis : The Effects on The European Stock Market. European Financial Management, 29(4), 1078-1118. http://dx.doi.org/10.1111/eufm.12386
Alotaibi, A. R., & Mishra, A. V. (2015). Global and Regional Volatility Spillovers to GCC Stock Markets. Economic Modelling, 45(February), 38-49. http://dx.doi.org/10.1016/j.econmod.2014.10.052
Anyikwa, I., & Phiri, A. (2023). Connectedness and Spillover between African Equity, Commodity, foreign Exchange and Cryptocurrency Markets during the COVID-19 and Russia-Ukraine Conflict. Future Business Journal, 9(1), 1-18. http://dx.doi.org/10.1186/s43093-023-00227-y
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636. http://dx.doi.org/10.1093/qje/qjw024
Balcilar, M., Bonato, M., Demirer, R., & Gupta, R. (2018). Geopolitical Risks and Stock Market Dynamics of the BRICS. Economic Systems, 42(2), 295-306. http://dx.doi.org/10.1016/j.ecosys.2017.05.008
Barunik, J., & Krehlik, T. (2016). Measuring the Frequency Dynamics of Financial and Macroeconomic Connectedness. Retrieved from Kiel:
Basdekis, C., Christopoulos, A., Katsampoxakis, I., & Nastas, V. (2022). The Impact of the Ukrainian War on Stock and Energy Markets : A Wavelet Coherence Analysis. Energies, 15(21), 1-15. http://dx.doi.org/10.3390/en15218174
Basdekis, C., Christopoulos, A. G., Katsampoxakis, I., & Xanthopoulos, S. (2024). Trends and Challenges after the Impact of COVID-19 and the Energy Crisis on Financial Markets. Energies, 17(15), 1-14. http://dx.doi.org/10.3390/en17153857
Belcaid, K., El Aoufi, S., & Al-Faryan, M. A. S. (2024). Dynamics of Contagion Risk Among World Markets in Times of Crises : A Financial Network Perspective. Asia-Pacific Financial Markets, 31(4), 1007-1033. http://dx.doi.org/10.1007/s10690-023-09439-2
Boungou, W., & Yatié, A. (2024). Uncertainty, Stock and Commodity Prices during the Ukraine-Russia War. Policy Studies, 45(3‑4), 336-352. http://dx.doi.org/10.1080/01442872.2024.2302440
Caldara, D., & Iacoviello, M. (2022). Measuring Geopolitical Risk. The American Economic Review, 112(4), 1194-1225. http://dx.doi.org/10.1257/aer.20191823
Chatziantoniou, I., Gabauer, D., & Stenfors, A. (2021). Interest Rate Swaps and the Transmission Mechanism of Monetary Policy : A Quantile Connectedness Approach. Economics Letters, 204(July), 1-4. http://dx.doi.org/10.1016/j.econlet.2021.109891
Chiang, T. C. (2021). Geopolitical Risk, Economic Policy Uncertainty and Asset Returns in Chinese Financial Markets. China Finance Review International, 11(4), 474-501. http://dx.doi.org/10.1108/CFRI-08-2020-0115
Chowdhury, E. K., Khan, I. I., & Dhar, B. K. (2022). Catastrophic Impact of Covid-19 on the Global Stock Markets and Economic Activities. Business and Society Review, 127(2), 437-460. http://dx.doi.org/10.1111/basr.12219
Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. Economic Journal (London), 119(534), 158-171. http://dx.doi.org/10.1111/j.1468-0297.2008.02208.x
Diebold, F. X., & Yilmaz, K. (2012). Better to Give than to Receive : Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28(1), 57-66. http://dx.doi.org/10.1016/j.ijforecast.2011.02.006
Diebold, F. X., & Yilmaz, K. (2014). On the Network Topology of Variance Decompositions : Measuring the Connectedness of Financial Firms. Journal of Econometrics, 182(1), 119-134. http://dx.doi.org/10.1016/j.jeconom.2014.04.012
Fang, Y., & Shao, Z. (2022). The Russia-Ukraine Conflict and Volatility Risk of Commodity Markets. Finance Research Letters, 50(3), 1-12. http://dx.doi.org/10.1016/j.frl.2022.103264
Fasanya, I. O., Adekoya, O. B., & Adetokunbo, A. M. (2021). On the Connection between Oil and Global Foreign Exchange Markets: The Role of Economic Policy Uncertainty. Resources Policy, 72(August), 1-10. http://dx.doi.org/10.1016/j.resourpol.2021.102110
Ghosh, I., Alfaro-Cortés, E., Gámez, M., & García-Rubio, N. (2024). Reflections of Public Perception of Russia-Ukraine Conflict and Metaverse on the Financial Outlook of Metaverse Coins : Fresh Evidence from Reddit Sentiment Analysis. International Review of Financial Analysis, 93(May), 1-12. http://dx.doi.org/10.1016/j.irfa.2024.103215
Gong, X., & Xu, J. (2022). Geopolitical Risk and Dynamic Connectedness between Commodity Markets. Energy Economics, 110(June), 1-15. http://dx.doi.org/10.1016/j.eneco.2022.106028
Hanif, W., Hadhri, S., & El Khoury, R. (2024). Quantile Spillovers and Connectedness between Oil Shocks and Stock Markets of the Largest Oil Producers and Consumers. Journal of Commodity Markets, 34(June), 1-33. http://dx.doi.org/10.1016/j.jcomm.2024.100404
Hu, Y., Corbet, S., Hou, Y. G., Lang, C., & Oxley, L. (2024). Tail Risk Connectedness during Geopolitical Shocks : Assessing the Impact of Russian-Ukraine Conflict on G7 Stock Markets. Applied Economics, 57(47), 7705–7733. http://dx.doi.org/10.1080/00036846.2024.2393893
Huang, S., Bossman, A., Gubareva, M., & Teplova, T. (2024). International Transmission of Shocks and African Forex Markets. Energy Economics, 131(March), 1-18. http://dx.doi.org/10.1016/j.eneco.2024.107382
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(January), 1-13. http://dx.doi.org/10.1016/j.resourpol.2023.104376
Koenker, R., & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. http://dx.doi.org/10.2307/1913643
Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse Response Analysis in Nonlinear Multivariate Models. Journal of Econometrics, 74(1), 119-147. http://dx.doi.org/10.1016/0304-4076(95)01753-4
Korsah, D., & Mensah, L. (2024). Geopolitical Risk, Economic Policy Uncertainty, Financial Stress and Stock Returns Nexus: Evidence from African Stock Markets. Journal of Capital Markets Studies, 8(1), 25-42. http://dx.doi.org/10.1108/JCMS-08-2023-0031
Le, T. H., & Luong, A. T. (2022). Dynamic Spillovers between Oil Price, Stock Market, and Investor Sentiment : Evidence from the United States and Vietnam. Resources Policy, 78(September), 1-12. http://dx.doi.org/10.1016/j.resourpol.2022.102931
Li, Z., Hu, B., Zhang, Y., & Yang, W. (2024). Financial Market Spillovers and Investor Attention to the Russia-Ukraine War. International Review of Economics & Finance, 96(November), 1-26. http://dx.doi.org/10.1016/j.iref.2024.103521
Liu, Q., Xu, C., & Xie, J. (2024). Comparative Analysis of Spillover Effects in the Global Stock Market under Normal and Extreme Market Conditions. International Journal of Financial Studies, 12(2), 1-20. http://dx.doi.org/10.3390/ijfs12020053
Muddasir, M., & Camino Ramon-Llorens, M. (2025). Impact of Macroeconomic Factors on S&P Europe 350 ESG Index During the Russo-Ukrainian War. Scientific Annals of Economics and Business, 72(1), 145–164. http://dx.doi.org/10.47743/saeb-2025-0008
Nafisi-Moghadam, M., Béjaoui, A., Jeribi, A., & Youssaf, I. (2025). A Catering Perspective of the Banking Sector Markets: Evidence from a Cross-Country Analysis. Scientific Annals of Economics and Business, 72(1), 125-144. http://dx.doi.org/10.47743/saeb-2025-0007
Nasouri, A. (2025). The Impact of Geopolitical Risks on Equity Markets and Financial Stress: A Comparative Analysis of Emerging and Advanced Economies. International Journal of Economics and Business Administration, XIII(1), 30-41. http://dx.doi.org/10.35808/ijeba/873
Nguyen, T. N., Phan, T. K. H., Nguyen, T. L., & McMillan, D. (2022). Financial Contagion During Global Financial Crisis and Covid–19 Pandemic: The Evidence from Dcc–Garch Model. Cogent Economics & Finance, 10(1), 1-20. http://dx.doi.org/10.1080/23322039.2022.2051824
NguyenHuu, T., Örsal, D. K., & NguyenHuu. (2024). Geopolitical Risks and Financial Stress in Emerging Economies. World Economy, 47(1), 217-237. http://dx.doi.org/10.1111/twec.13529
Pesaran, H. H., & Shin, Y. (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Economics Letters, 58(1), 17-29. http://dx.doi.org/10.1016/S0165-1765(97)00214-0
Smales, L. A. (2020). Examining the Relationship between Policy Uncertainty and Market Uncertainty Across the G7. International Review of Financial Analysis, 71(October ), 1-17. http://dx.doi.org/10.1016/j.irfa.2020.101540
Soltani, H., & Boujelbene Abbes, M. (2023). The Impact of the COVID-19 Pandemic on the Nexus between the Investor’s Sentiment and the Financial Market Dynamics : Evidence from the Chinese Market. Asia-Pacific Journal of Business Administration, 15(5), 673-694. http://dx.doi.org/10.1108/APJBA-07-2021-0326
Soltani, H., & Boujelbène Abbes, M. (2025). The Spillover Effects Among Financial Stress, Investor Sentiment, and GCC Stock Markets: Evidence under the Bearish and Bullish Market States. Journal of Chinese Economic and Business Studies, 23(1), 1-25. http://dx.doi.org/10.1080/14765284.2024.2404278
Soltani, H., Boujelbene Abbes, M., & Aloulou, A. (2021). Investigating the Relationship Between Financial Stress Index and MENA Stock Markets Performance. International Journal of Finance and Banking Research, 7(5), 111-122. http://dx.doi.org/10.11648/j.ijfbr.20210705.12
Soltani, H., Taleb, J., & Abbes, M. B. (2025). The Directional Spillover Effects and Time-Frequency Nexus between Stock Markets, Cryptocurrency, and Investor Sentiment during the COVID-19 Pandemic. European Journal of Management and Business Economics, 34(1), 23-46. http://dx.doi.org/10.1108/EJMBE-09-2022-0305
Tong, E. (2024). Repercussions of the Russia–Ukraine war. International Review of Economics & Finance, 89(January), 366-390. http://dx.doi.org/10.1016/j.iref.2023.07.064
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(3), 284-305. http://dx.doi.org/10.32609/j.ruje.9.105833
Ullah, M., Sohag, K., Nawaz, F., Mariev, O., Kayani, U., Mayburov, I., & Doroshenko, S. (2024). Impact of Oil Price Shocks on Crypto and Conventional Financial Assets during Financial Crises : Evidence from the Russian Financial Market. International Journal of Energy Economics and Policy, 14(4), 472–483. http://dx.doi.org/10.32479/ijeep.16374
Umar, Z., Gubareva, M., Naeem, M., & Akhter, A. (2021). Return and Volatility Transmission between Oil Price Shocks and Agricultural Commodities. PLoS One, 16(2), 1-18. http://dx.doi.org/10.1371/journal.pone.0246886
Wang, Y., Bouri, E., Fareed, Z., & Dai, Y. (2022). Geopolitical Risk and the Systemic Risk in the Commodity Markets under the War in Ukraine. Finance Research Letters, 49(October), 1-20. http://dx.doi.org/10.1016/j.frl.2022.103066
White, H., Kim, T. H., & Manganelli, S. (2015). VAR for VaR : Measuring Tail Dependence Using Multivariate Regression Quantiles. Journal of Econometrics, 187(1), 169-188. http://dx.doi.org/10.1016/j.jeconom.2015.02.004
Yousaf, I., Youssef, M., & Goodell, J. W. (2022). Quantile Connectedness between Sentiment and Financial Markets: Evidence from the S&P 500 Twitter Sentiment Index. International Review of Financial Analysis, 83(October), 1-15. http://dx.doi.org/10.1016/j.irfa.2022.102322
Yuan, D., Li, S., Li, R., & Zhang, F. (2022). Economic Policy Uncertainty, Oil and Stock Markets in BRIC: Evidence from Quantiles Analysis. Energy Economics, 110(June), 1-17. http://dx.doi.org/10.1016/j.eneco.2022.105972
Zhang, W., & Hamori, S. (2021). The Connectedness between the Sentiment Index and Stock Return Volatility under Covid-19: A Time-Varying Parameter Vector Autoregression Approach. The Singapore Economic Review(December), 1-32. http://dx.doi.org/10.1142/S0217590822500023
Zhang, W., He, X., Nakajima, T., & Hamori, S. (2020). How Does the Spillover among Natural Gas, Crude Oil, and Electricity Utility Stocks Change over Time? Evidence from North America and Europe. Energies, 13(3), 1-26. http://dx.doi.org/10.3390/en13030727
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Copyright (c) 2025 Hayet Soltani, Amel Ben Ameur, Mouna Boujelbène Abbes
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