Geopolitical risks, market volatility, and tech firms involved in quantum computing

DOI: https://doi.org/10.3846/jbem.2026.26193

Abstract

This study examines how global uncertainty influences the financial dynamics of technology firms involved in quantum computing, a strategically significant but structurally fragile segment of emerging deep-tech markets. Using daily data from January 2015 to May 2025, the analysis integrates principal component decomposition, panel regression, Granger causality testing and volatility diagnostics to assess the transmission of market volatility and geopolitical risk. The findings show that market volatility, proxied by the VIX index, exerts a persistent and adverse influence on stock returns, confirming its role as a systemic risk factor. Geopolitical risk, measured through the ACT and THREAT sub-indices of the Geopolitical Risk Index (GPR), also affects return behaviour, but through asymmetric and time-varying transmission mechanisms that emerge under heightened uncertainty and global strategic tension. The results further reveal heterogeneous vulnerability profiles across firms, indicating conditional risk spillovers rather than uniform market reactions. The study contributes new empirical evidence on the interplay between financial and geopolitical risk in advanced technology sectors and offers a replicable framework for uncertainty modelling in frontier markets.

Keywords:

quantum computing, volatility, geopolitical risk, VIX, principal component analysis, clustering

How to Cite

Panazan, O., & Gheorghe, C. (2026). Geopolitical risks, market volatility, and tech firms involved in quantum computing. Journal of Business Economics and Management, 27(1), 118–141. https://doi.org/10.3846/jbem.2026.26193

Share

Published in Issue
February 27, 2026
Abstract Views
119

References

Ali, S., Moussa, F., & Youssef, M. (2023). Connectedness between cryptocurrencies using high-frequency data: A novel insight from the Silicon Valley Banks collapse. Finance Research Letters, 58, Article 104352. https://doi.org/10.1016/j.frl.2023.104352

Alnafisah, H., Almansour, B. Y., Elabed, W., & Jeribi, A. (2025). Spillover dynamics of digital assets during economic and political crises. Research in International Business and Finance, 75, Article 102770. https://doi.org/10.1016/j.ribaf.2025.102770

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

Assaf, A., Demir, E., & Ersan, O. (2024). What drives the return and volatility spillover between DeFis and cryptocurrencies? International Journal of Finance & Economics, 30(2), 1302–1318. https://doi.org/10.1002/ijfe.2969

Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343. https://doi.org/10.1016/S0304-405X(98)00027-0

Ben Jabeur, S., Gozgor, G., Rezgui, H., & Mohammed, K. S. (2024). Dynamic dependence between quantum computing stocks and Bitcoin: Portfolio strategies for a new era of asset classes. International Review of Financial Analysis, 95, Article 103478. https://doi.org/10.1016/j.irfa.2024.103478

Bloomberg. (n.d.). Homepage. https://www.bloomberg.com/

Bova, F., Goldfarb, A., & Melko, R. G. (2023). Quantum economic advantage. Management Science, 69(2), 1116–1126. https://doi.org/10.1287/mnsc.2022.4578

Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823

Chatterjee, S., Chaudhuri, R., Kar, A. K., & Dwivedi, Y. K. (2023). Adoption of artificial intelligence and cutting-edge technologies for production system sustainability: A moderator-mediation analysis. Information Systems Frontiers, 25(5), 1779–1794. https://doi.org/10.1007/s10796-022-10317-x

Chen, H., Gong, X., & Li, Z. (2026). Trade policy uncertainty, changing investment behavior, and industrial development in China: Evidence from the US–China trade war. Journal of Development Economics, 170, Article 103237.

Chen, Z., Liu, Y., & Zhang, H. (2024). Can geopolitical risks impact the long-run correlation between crude oil and clean energy markets? Renewable Energy, 229, Article 120774. https://doi.org/10.1016/j.renene.2024.120774

Chowdhury, M. A. F., Abdullah, M., Alam, M., Abedin, M. Z., & Shi, B. (2023). NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis. International Review of Financial Analysis, 87, Article 102642. https://doi.org/10.1016/j.irfa.2023.102642

Coccia, M., & Roshani, S. (2025). Path-breaking directions in quantum computing technology: A patent analysis with multiple techniques. Journal of the Knowledge Economy, 16, 4991–5024. https://doi.org/10.1007/s13132-024-01977-y

Coccia, M., Roshani, S., & Mosleh, M. (2022). Evolution of quantum computing: Theoretical and innovation management implications for emerging quantum industry. IEEE Transactions on Engineering Management, 71, 2270–2280. https://doi.org/10.1109/TEM.2022.3175633

CompaniesMarketCap. (n.d.). Largest tech companies by market cap. https://companiesmarketcap.com/tech/largest-tech-companies-by-market-cap/

Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton University Press.

Dong, X., Gong, J., & Wang, Q. (2025). Environmental attention and the predictability of crude oil volatility: Evidence from a new MIDAS multifractal model. Energy Economics, 143, Article 108227. https://doi.org/10.1016/j.eneco.2025.108227

Epstein, L. G., & Schneider, M. (2008). Ambiguity, information quality, and asset pricing. The Journal of Finance, 63(1), 197–228. https://doi.org/10.1111/j.1540-6261.2008.01314.x

Ghaemi Asl, M., Ben Jabeur, S., Nammouri, H., & Bel Hadj Miled, K. (2024). Dynamic connectedness of quantum computing, artificial intelligence, and big data stocks on renewable and sustainable energy. Energy Economics, 140, Article 108017. https://doi.org/10.1016/j.eneco.2024.108017

Hoekstra, J., & Güler, D. (2024). The mediating effect of trading volume on the relationship between investor sentiment and the return of tech companies. Journal of Behavioral Finance, 25(3), 356–373. https://doi.org/10.1080/15427560.2022.2138394

Hsiao, C. (2014). Analysis of panel data (3rd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139839327

Investing. (n.d.). Homepage. https://www.investing.com/

Khan, N., Mejri, S., & Hammoudeh, S. (2024). How do global commodities react to increasing geopolitical risks? New insights into the Russia–Ukraine and Palestine–Israel conflicts. Energy Economics, 138, Article 107812. https://doi.org/10.1016/j.eneco.2024.107812

Lan, Y., Xian, J., & Bai, N. (2024). Digital technology adoption and investment sensitivity to stock price. Economics Letters, 243, Article 111935. https://doi.org/10.1016/j.econlet.2024.111935

Liang, C., Luo, Q., Li, Y., & Huynh, L. D. T. (2023). Global financial stress index and long-term volatility forecast for international stock markets. Journal of International Financial Markets, Institutions and Money, 88, Article 101825. https://doi.org/10.1016/j.intfin.2023.101825

Liu, L., Shahrour, M. H., Wojewodzki, M., & Rohani, A. (2025). Decoding energy market turbulence: A TVP-VAR connectedness analysis of climate policy uncertainty and geopolitical risk shocks. Technological Forecasting and Social Change, 210, Article 123863. https://doi.org/10.1016/j.techfore.2024.123863

Lucey, B., & Ren, B. (2023). Time-varying tail risk connectedness among sustainability-related products and fossil energy investments. Energy Economics, 126, Article 106812. https://doi.org/10.1016/j.eneco.2023.106812

Luo, Q., Ma, F., Wang, J., & Wu, Y. (2024). Changing determinant driver and oil volatility forecasting: A comprehensive analysis. Energy Economics, 129, Article 107187. https://doi.org/10.1016/j.eneco.2023.107187

Martín-Guerrero, J. D., & Lamata, L. (2022). Quantum machine learning: A tutorial. Neurocomputing, 470, 457–461. https://doi.org/10.1016/j.neucom.2021.02.102

Núñez-Merino, M., Maqueira-Marín, J. M., Moyano-Fuentes, J., & Castaño-Moraga, C. A. (2024). Quantum-inspired computing technology in operations and logistics management. International Journal of Physical Distribution & Logistics Management, 54(3), 247–274. https://doi.org/10.1108/IJPDLM-02-2023-0065

Pandey, D. K., Hassan, M. K., Kumari, V., & Hasan, R. (2023). Repercussions of the Silicon Valley Bank collapse on global stock markets. Finance Research Letters, 55(Part B), Article 104013. https://doi.org/10.1016/j.frl.2023.104013

Parnes, D., & Gormus, A. (2024). Prescreening bank failures with K-means clustering: Pros and cons. International Review of Financial Analysis, 93, Article 103222. https://doi.org/10.1016/j.irfa.2024.103222

Pham, H. T., & Hsu, S.-H. (2025). Metals of the future in a world in crisis: Geopolitical disruptions and the cleantech metal industry. Energy Economics, 141, Article 108004. https://doi.org/10.1016/j.eneco.2024.108004

Piñeiro-Chousa, J., López-Cabarcos, M. Á., Sevic, A., & González-López, I. (2022). A preliminary assessment of the performance of DeFi cryptocurrencies in relation to other financial assets, volatility, and user-generated content. Technological Forecasting and Social Change, 181, Article 121740. https://doi.org/10.1016/j.techfore.2022.121740

Prasad, E. (2023). How will digital technologies influence the international monetary system? Oxford Review of Economic Policy, 39(2), 389–397. https://doi.org/10.1093/oxrep/grad011

Putranto, D. S. C., Wardhani, R. W., Ji, J., & Kim, H. (2024). A deep inside quantum technology industry: Trends and future implications. IEEE Access, 12, 115776–115792. https://doi.org/10.1109/ACCESS.2024.3444779

Riel, H. (2022). On the potentials of quantum computing – An interview with Heike Riel from IBM Research. Electronic Markets, 33(1), 135–146. https://doi.org/10.1007/s12525-022-00616-1

Seskir, Z. C., Kaur, M., Venegas-Gomez, A., Purohit, A., & Posner, T. (2022). Building a quantum-ready ecosystem: Opportunities and challenges in the quantum technology landscape. Financial Innovation, 8(1), Article 146.

Shahroodi, K., Darestani, S. A., Soltani, S., & Saravani, A. E. (2024). Developing strategies to retain organizational insurers using a clustering technique: Evidence from the insurance industry. Technological Forecasting and Social Change, 201, Article 123217. https://doi.org/10.1016/j.techfore.2024.123217

Si Mohammed, K., Khalfaoui, R., Doğan, B., Sharma, G. D., & Mentel, U. (2023). The reaction of the metal and gold resource planning in the post-COVID-19 era and Russia-Ukrainian conflict: Role of fossil fuel markets for portfolio hedging strategies. Resources Policy, 83, Article 103654. https://doi.org/10.1016/j.resourpol.2023.103654

Tiwari, S., & Si Mohammed, K. (2025). Fusion of fintech and green finance amidst Russo-Ukrainian conflict: A step toward sustainable development. Sustainable Development, 33(5), 6936–6953. https://doi.org/10.1002/sd.3502

Tripathi, V. V. R., Srivastava, M. K., Jaiswal, R., Singh, T. D., & Khaled, A. S. (2024). Marketing logistics and consumer behaviour: An empirical study on Indian e-shoppers. Cogent Business & Management, 11(1), Article 2397559. https://doi.org/10.1080/23311975.2024.2397559

Umbrello, S., Seskir, Z. C., & Vermaas, P. E. (2024). Communities of quantum technologies: Stakeholder identification, legitimation, and interaction. International Journal of Quantum Information, 22(7), Article 2450012. https://doi.org/10.1142/S0219749924500126

Urom, C., Ndubuisi, G., Mzoughi, H., & Guesmi, K. (2024). Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations. Financial Innovation, 10, Article 128. https://doi.org/10.1186/s40854-024-00609-3

Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12–17. https://doi.org/10.3905/jpm.2000.319728

Yousfi, M., & Bouzgarrou, H. (2024). Economic uncertainty, political tension and volatility connectedness among clean energy, conventional energy, and food markets. Energy Economics, 137, Article 107683. https://doi.org/10.1016/j.eneco.2024.107683

Zhang, X., & Wu, S. (2024). Natural resources and sustainable development: Evidence from the dynamic correlation between crude oil and gold market. International Review of Economics & Finance, 96(Part B), Article 103665. https://doi.org/10.1016/j.iref.2024.103665

View article in other formats

CrossMark check

CrossMark logo

Published

2026-02-27

Issue

Section

Articles

How to Cite

Panazan, O., & Gheorghe, C. (2026). Geopolitical risks, market volatility, and tech firms involved in quantum computing. Journal of Business Economics and Management, 27(1), 118–141. https://doi.org/10.3846/jbem.2026.26193

Share