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The dynamics of volatility spillovers among Russian economy sectors
https://doi.org/10.32609/0042-8736-2024-12-50-68
Abstract
Volatility is an indicator of the risk existing in the economy, and its volume characterizes the magnitude of risks transmitted from one sector to another. The objective of the study is to determine the dynamics of volatility spillovers among sectors of the Russian economy during crisis periods and classify sectors into shock transmitters and shock receivers. The daily returns of the Moscow Exchange sector indices for 2018-2023 acted as data. The Diebold-Yilmaz methodology based on the VAR model is used to determine the dynamics of volatility spillovers. The study has revealed that the nature of volatility spillovers differs in the pre-crisis period, during the COVID-19 pandemic, and during the special military operation (SMO). The financial sector is a source of volatility spillovers in the first and the last periods. During the pandemic, the oil and gas and transport sectors become volatility receivers. During the period of the SMO, the metallurgy and petrochemical sectors act as receivers of volatility spillovers, while the consumer goods and financial sectors act as their sources.
About the Authors
Yu. V. Kudryavtseva
Bank of Russia
Russian Federation
Russian Federation
Yulia V. Kudryavtseva.
Moscow
A. G. Mirzoyan
Lomonosov Moscow State University
Russian Federation
Russian Federation
Ashot G. Mirzoyan.
Moscow
References
1. Lomivorotov R. V. (2015). Using Bayesian methods to analyze monetary policy in Russia. Applied Econometrics, No. 2, pp. 41—63. (In Russian).
2. Naidenova I. N., Leonteva V. V. (2020). Economic policy uncertainty and investment of Russian companies. Voprosy Ekonomiki, No. 2, pp. 141—159. (In Russian). https://doi.org/10.32609/0042-8736-2020-2-141-159
3. FRI (2019). Review of global energy markets: Oil market, January—February 2019. Moscow: Financial Research Institute of the Ministry of Finance of the Russian Federation. (In Russian).
4. Adekoya O. B., Oliyide J. A., Noman A. (2021). The volatility connectedness of the EU carbon market with commodity and financial markets in time- and frequency-domain: The role of the U.S. economic policy uncertainty. Resources Policy, Vol. 74, article 102252. https://doi.org/10.1016/j.resourpol.2021.102252
5. Ashraf B. N. (2020). Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets. Journal of Behavioral and Experimental Finance, Vol. 27, article 100371. https://doi.org/10.1016/j.jbef.2020.100371
6. Baig A. S., Butt H. A., Haroon O., Rizvi S. A. R. (2021). Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic. Finance Research Letters, Vol. 38, No. C, article 101701. https://doi.org/10.1016/j.frl.2020.101701
7. Baker S. R., Bloom N., Davis S. J., Kost K., Sammon M., Viratyosin T. (2020). The unprecedented stock market reaction to COVID-19. Review of Asset Pricing Studies, Vol. 10, No. 4, pp. 742—758. https://doi.org/10.1093/rapstu/raaa008
8. Baruník J., Kočenda E., Vácha L. S. (2015). Volatility spillovers across petroleum markets. Energy Journal, Vol. 36, No. 3, pp. 309—329. https://doi.org/10.5547/01956574.36.3.jbar
9. Baruník J., Kočenda E., Vácha L. (2016). Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. Journal of Financial Markets, Vol. 27, pp. 55—78. https://doi.org/10.1016/j.finmar.2015.09.003
10. Bond P., Edmans A., Goldstein I. (2012). The real effects of financial markets. Annual Review of Financial Economics, Vol. 4, pp. 339—360. https://doi.org/10.1146/annurev-financial-110311-101826
11. Bouri E., Naeem M. A., Nor S. M., Mbarki I., Saeed T. (2022). Government responses to COVID-19 and industry stock returns. Economic Research—Ekonomska Istraživanja, Vol. 35, No. 1, pp. 1967—1990. https://doi.org/10.1080/1331677X.2021.1929374
12. Chechenova L. M. (2020). Increasing the investment attractiveness of transport organizations engaged in intermodal container transport during the pandemic crisis. Transportation Systems and Technology, Vol. 6, No. 4, pp. 98—112. https://doi.org/10.17816/TRANSSYST20206498-112
13. Chen Y., Li W., Qu F. (2019). Dynamic asymmetric spillovers and volatility interdependence on China’s stock market. Physica A: Statistical Mechanics and Its Applications, Vol. 523, No. C, pp. 825—838. https://doi.org/10.1016/j.physa.2019.02.021
14. Chen Y., Zheng B., Qu F. (2020). Modeling the nexus of crude oil, new energy and rare earth in China: An asymmetric VAR-BEKK (DCC)-GARCH approach. Resources Policy, Vol. 65, article 101545. https://doi.org/10.1016/j.resourpol.2019.101545
15. Choi K. H., McIver R. P., Ferraro S., Xu L., Kang S. H. (2021). Dynamic volatility spillover and network connectedness across ASX sector markets. Journal of Economics and Finance, Vol. 45, No. 4, pp. 677—691 https://doi.org/10.1007/s12197-021-09544-w
16. Choi S. Y. (2022). Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday. North American Journal of Economics and Finance, Vol. 59, article 101614. https://doi.org/10.1016/j.najef.2021.101614
17. Dang T. H. N., Nguyen N. T., Vo D. H. (2023). Sectoral volatility spillovers and their determinants in Vietnam. Economic Change and Restructuring, Vol. 56, No. 1, pp. 681—700. https://doi.org/10.1007/s10644-022-09446-9
18. Diebold F. X., Yilmaz K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, Vol. 28, No. 1, pp. 57—66. https://doi.org/10.1016/j.ijforecast.2011.02.006
19. Diebold F. X., Yilmaz K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, Vol. 182, No. 1, pp. 119—134. https://doi.org/10.1016/j.jeconom.2014.04.012
20. Diebold F. X., Yilmaz K. (2016). Trans-Atlantic volatility connectedness among financial institutions. Journal of Financial Econometrics, Vol. 14, No. 1, pp. 81—127. Diebold F. X., Yilmaz K. (2023). On the past, present, and future of the Diebold—Yilmaz approach to dynamic network connectedness. Journal of Econometrics, Vol. 234, pp. 115—120. https://doi.org/10.1016/j.jeconom.2023.01.021
21. Fama E. F. (1990). Stock returns, expected returns, and real activity. Journal of Finance, Vol. 45, No. 4, pp. 1089—1108. https://doi.org/10.2307/2328716
22. Fasanya I. O., Oyewole O., Agbatogun T. (2019). Measuring return and volatility spill-overs among sectoral stocks in Nigeria. Zagreb International Review of Economics and Business, Vol. 22, No. 2, pp. 71—93. https://doi.org/10.2478/zireb-2019-0021
23. Gao Y., Zheng W., Wang Y. (2023). Sectoral risk contagion and quantile network connectedness on Chinese stock sectors after the COVID-19 outbreak. China Finance Review International. https://doi.org/10.1108/CFRI-02-2023-0039
24. Goldstein I. (2023). Information in financial markets and its real effects. Review of Finance, Vol. 27, No. 1, pp. 1—32. https://doi.org/10.1093/rof/rfac052
25. Izzeldin M., Muradoğlu Y. G., Pappas V., Petropoulou A., Sivaprasad S. (2023). The impact of the Russian—Ukrainian war on global financial markets. International Review of Financial Analysis, Vol. 87, article 102598. https://doi.org/10.1016/j.irfa.2023.102598
26. Kakran S., Sidhu A., Bajaj P. K., Dagar V. (2023). Novel evidence from APEC countries on stock market integration and volatility spillover: A Diebold and Yilmaz approach. Cogent Economics and Finance, Vol. 11, No. 2, article 2254560. https://doi.org/10.1080/23322039.2023.2254560
27. Kang S. H., Maitra D., Dash S. R., Brooks R. (2019). Dynamic spillovers and connectedness between stock, commodities, bonds, and VIX markets. Pacific Basin Finance Journal, Vol. 58, article 101221. https://doi.org/10.1016/j.pacfin.2019.101221
28. Kočenda E. (2018). Survey of volatility and spillovers on financial markets. Prague Economic Papers, Vol. 27, No. 3, pp. 293—305. https://doi.org/10.18267/j.pep.650
29. Laopodis N. T., Papastamou A. (2016). Dynamic interactions between stock markets and the real economy: Evidence from emerging markets. International Journal of Emerging Markets, Vol. 11, No. 4, pp. 715—746. https://doi.org/10.1108/IJoEM-12-2015-0253
30. 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, Vol. 73, No. C, article 102166. https://doi.org/10.1016/j.resourpol.2021.102166
31. Lou Y., Xiao C., Lian Y. (2024). Dynamic asymmetric spillovers and connectedness between Chinese sectoral commodities and industry stock markets. PLoS ONE, Vol. 19, article e0296501. https://doi.org/10.1371/journal.pone.0296501
32. Pástor L., Veronesi P. (2012). Uncertainty about government policy and stock prices. Journal of Finance, Vol. 67, No. 4, pp. 1219—1264. https://doi.org/10.1111/j.1540-6261.2012.01746.x
33. Pearce D. K. (1983). Stock prices and the economy. Federal Reserve Bank of Kansas City Economic Review, Vol. 68, No. 9, pp. 7—22.
34. Shahzad S. J. H., Naeem M. A., Peng Z., Bouri E. (2021). Asymmetric volatility spill-over among Chinese sectors during COVID-19. International Review of Financial Analysis, Vol. 75, article 101754. https://doi.org/10.1016/j.irfa.2021.101754
35. Shigemoto H., Morimoto T. (2022). Volatility spillover among Japanese sectors in response to COVID-19. Journal of Risk and Financial Management, Vol. 15, No. 10, article 480. https://doi.org/10.3390/jrfm15100480
36. Su X., Liu Z. (2021). Sector volatility spillover and economic policy uncertainty: Evidence from China’s stock market. Mathematics, Vol. 9, No. 12, pp. 1—22. https://doi.org/10.3390/math9121411
37. Syriopoulos T., Makram B., Boubaker A. (2015). Stock market volatility spillovers and portfolio hedging: BRICS and the financial crisis. International Review of Financial Analysis, Vol. 39, May, pp. 7—18. https://doi.org/10.1016/j.irfa.2015.01.015
38. Uddin G. S., Shahzad S. J. H., Boako G., Hernandez J. A., Lucey B. M. (2019). Heterogeneous interconnections between precious metals: Evidence from asymmetric and frequency-domain spillover analysis. Resources Policy, Vol. 64, No. C, article 101509. https://doi.org/10.1016/j.resourpol.2019.101509
39. Wu Y., Ren W., Wan J., Liu X. (2023). Time-frequency volatility connectedness between fossil energy and agricultural commodities: Comparing the COVID-19 pandemic with the Russia—Ukraine conflict. Finance Research Letters, Vol. 55, article 103866. https://doi.org/10.1016/j.frl.2023.103866
40. Xie W., Zhang H., Guo J., He M. (2022). Does a national industrial policy promote financial market stability? A study based on stock price crash risk. China Journal of Accounting Research, Vol. 15, No. 4, article 100269. https://doi.org/10.1016/j.cjar.2022.100269
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For citations:
Kudryavtseva Yu.V., Mirzoyan A.G. The dynamics of volatility spillovers among Russian economy sectors. Voprosy Ekonomiki. 2024;(12):50-68. (In Russ.) https://doi.org/10.32609/0042-8736-2024-12-50-68
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