The influence of external factors on excess option volatility
- Authors: Zhironkin S.A.1, Konovalova M.E.2, Kuzmina O.Y.2, Aleksandrov D.P.3
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Affiliations:
- Siberian Federal University
- Samara State University of Economics
- HSE University
- Issue: No 3 (2025)
- Pages: 7-20
- Section: Articles
- URL: https://vektornaukieconomika.ru/jour/article/view/881
- DOI: https://doi.org/10.18323/3034-2074-2025-3-62-1
- ID: 881
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Abstract
In the context of increasing geopolitical and macroeconomic instability, identifying and accounting for factors influencing option volatility is of particular interest to practicing investors. Over the past few years, the volumes of option trading have demonstrated steady growth, surpassing the volumes of futures trading, demonstrating the growing role of options in the structure of financial markets. High turbulence of economic processes leads to low risk management efficiency in options trading. This problem can be solved through a more accurate assessment of volatility price formation factors. The use of modern economic statistical analysis methods, including vector autoregressive (VAR) models, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and machine learning techniques, allowed carrying out a comprehensive analysis aimed at identifying relevant variables and developing approaches to incorporating them in option volatility modeling. The authors focused on expanding and deepening scientific understanding of the nature and mechanisms of option volatility formation in the face of environmental instability. It is proved that geopolitical shocks and economic uncertainty have a significant impact on volatility, and that this impact is asymmetric: emerging markets demonstrate greater sensitivity to these factors than developed markets. The results and conclusions obtained can be used to forecast excess option volatility, which, when taken into account, will allow developing more profitable investment strategies.
About the authors
Sergey A. Zhironkin
Siberian Federal University
Email: zhironkin@inbox.ru
ORCID iD: 0000-0002-0887-5907
Doctor of Sciences (Economics), Professor, professor of Chair of Commerce and Marketing
Russian Federation, 660041, Krasnoyarsk, Svobodny Prospekt, 79Mariya E. Konovalova
Samara State University of Economics
Email: mkonoval@mail.ru
ORCID iD: 0000-0002-1876-8144
Doctor of Sciences (Economics), Professor, Director of the Institute of National and Global Economy
Russian Federation, 443090, Samara, Sovetskoy Armii Street, 141Olga Yu. Kuzmina
Samara State University of Economics
Email: pisakina83@yandex.ru
ORCID iD: 0000-0002-4460-0468
PhD (Economics), Associate Professor, assistant professor of Chair of Economic Theory
Russian Federation, 443090, Samara, Sovetskoy Armii Street, 141Dmitry P. Aleksandrov
HSE University
Author for correspondence.
Email: dmitriy.aleksandrov.2003@mail.ru
graduate student
Russian Federation, 101000, Moscow, Myasnitskaya Street, 20References
- Khoranyan M.E. Historical and expected volatility models: genesis and application. Vestnik of the Plekhanov Russian university of economics. Introduction. The road to science, 2022, vol. 12, no. 2, pp. 47–58. EDN: MHDWLT.
- Gayomey D., Zaytsev A. Forecasting the volatility of us stock market indexes using GARCH models and high frequency volatility estimators. Scientific Journal Economic Sciences, 2022, no. 208, pp. 49–57. doi: 10.14451/1.208.49.
- Semernina Yu.V., Kiselev M.V., Yakunin S.V., Yakunina A.V. Comparative characteristics of futures and forward contracts. Menedzhment proizvodnykh finansovykh instrumentov. Saratov, Rossiyskiy ekonomicheskiy universitet im. G.V. Plekhanova Publ., 2020, pp. 48–51.
- Arraut I., Ka-I Lei. The Role of the Volatility in the Option Market. AppliedMath, 2023, vol. 3, no. 4, pp. 882–908. doi: 10.3390/appliedmath3040047.
- Gotfrid A.O., Guzikova L.A. A pre-predictive analysis of trading volume in the options market. Finance and Credit, 2021, vol. 27, no. 9, pp. 1962–1979. doi: 10.24891/fc.27.9.1962.
- Wang Guodong. Pricing of vulnerable option under affine stochastic volatility with simultaneous jumps model. Journal of Computational and Applied Mathematics, 2025, vol. 475, article number 117033. doi: 10.1016/j.cam.2025.117033.
- Atroshchenko S.A., Pervushkina E.A., Statuev A.A., Volodin A.M., Khorkin D.A. A mathematical model of volatility forecasting. Science and Business: Ways of Development, 2024, no. 6, pp. 10–15. EDN: MTFLBK.
- Souto H.G., Moradi A. Yang & Zhang’s realized volatility: Automated estimation in Python. Software Impacts, 2024, vol. 19, article number 100613. doi: 10.1016/j.simpa.2024.100613.
- Caplan B. How Does War Shock the Economy? Journal of International Money and Finance, 2002, vol. 21, no. 2, pp. 145–162. doi: 10.1016/S0261-5606(01)00046-8.
- Cherevko V.E. The military-industrial complex as a factor in the growth of the national economy. Economy and Business: Theory and Practice, 2024, no. 7, pp. 222–225. doi: 10.24412/2411-0450-2024-7-222-225.
- Chatterjee U. Economic Policy Uncertainty, World Uncertainty, and Economic Growth: Evidence from a Bayesian Vector Autoregression Analysis. International Business Research, 2023, vol. 16, no. 8, article number 28. doi: 10.5539/ibr.v16n8p28.
- Klement J. Geoeconomics: The interplay between geopolitics, economics, and investment. New York, CFA Institute Research Foundation Publ., 2021. 290 p. URL: https://www.cfainstitute.org/-/media/documents/book/rf-publication/2021/geo-economics-full.ashx.
- Gallyamov T.F., Savelov G.A., Kalach G.P. The impact of wars on financial markets. Economy and Security, 2025, no. 4, pp. 108–111. EDN: TXYVXN.
- Chirkov M.A., Shapovalova A.V., Chistyakov M.S. The Russian stock market under conditions of a special military operation and escalation of sanctions pressure of the Anglo-Saxon coalition. Ekonomika i predprinimatelstvo, 2023, no. 6, pp. 176–187. doi: 10.34925/EIP.2023.155.6.029.
- Guidolin M., Ferrara E.A. The Economic Effects of Violent Conflict: Evidence from Asset Market Reactions. Journal of Peace Research, 2010, vol. 47, no. 6, pp. 671–684. doi: 10.2139/ssrn.825889.
- Hassani H., Yeganegi M.R., Gupta R. Historical Forecasting of Interest Rate Mean and Volatility of the United States: Is There a Role of Uncertainty? Annals of Financial Economics, 2021, vol. 15, no. 4, article number 2050018. doi: 10.1142/S2010495220500189.
- Brune A., Hens T., Rieger M.O., Wang Mei. The War Puzzle: Contradictory Effects of International Conflicts on Stock Markets. International Review of Economics, 2015, vol. 62, no. 1, pp. 1–21. doi: 10.1007/s12232-014-0215-7.
- Li Xiongying, Ye Cheng, Bhuiyan M.A., Huang Shuiren. Volatility forecasting with an extended GARCH-MIDAS approach. Journal of Forecasting, 2023, vol. 43, no. 1, pp. 24–39. doi: 10.1002/for.3023.
- Domashchenko G.A., Ivina E.S. The role of gold in the transforming global financial system. Russian Journal of Social Sciences and Humanities, 2025, vol. 19, no. 2, pp. 171–184. EDN: EEFNPJ.
- Baur D.G., Smales L.A. Gold and Geopolitical Risk. 2018. URL: https://ssrn.com/abstract=3109136.
- Saradzheva O.V., Kovtun M.A. Challenges to the banking industry from an economic security perspective. Vestnik of economic security, 2020, no. 2, pp. 301–302. EDN: DIGMUG.
- Blomberg S.B., Hess G.D., Orphanides A. The Macroeconomic Consequences of Terrorism. Journal of Monetary Economics, 2004, vol. 51, no. 5, pp. 1007–1032. doi: 10.2139/ssrn.525982.
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