Russian invasion 2022: analysis of persistent volatility and return spillovers among IMOEX, WTI and Russian OT (10Y)
Abstract
Russia's invasion of Ukraine is creating instability in the financial markets, with European stock markets falling, and the effects reflected in energy and food prices. A war scenario brings with it a humanitarian crisis and it is the most vulnerable who suffer the worst consequences. Based on these events it is intended in this paper to test the persistence of returns on the IMOEX capital market, Russian Sovereign OT (10 YR), and the WTI oil index over the period April 24th, 2017, to April 22nd, 2022. To perform this analysis different approaches were undertaken to analyse, if: (i) do the analysed markets exhibit persistence in their returns? The results suggest that the returns do not follow the i.i.d. hypothesis from dimension 2, reinforcing the idea that time series returns are nonlinear in nature or have a significant nonlinear component, except for the Russian capital market, which was expected considering the results of the Ljung-Box (with squares of the returns) and ARCH-LM tests. These findings allow the creation of efficient portfolio diversification strategies, opening room for market regulators to take steps to ensure better informational information for investors operating in these financial markets.
References
Aggarwal, D. (2018). Random walk model and asymmetric effect in Korean composite stock price index. Afro-Asian J. of Finance and Accounting. https://doi.org/10.1504/aajfa.2018.10009906
Bagão, M., Dias, R., Heliodoro, P., & Alexandre, P. (2020). the Impact of Covid-19 on European Financial Markets: an Empirical Analysis. 6th LIMEN Conference Proceedings (Part of LIMEN Conference Collection), 6(July), 1–11. https://doi.org/10.31410/limen.2020.1
Breitung, J. (2000). The local power of some unit root tests for panel data. Advances in Econometrics. https://doi.org/10.1016/S0731-9053(00)15006-6
Brock, W. A., & de Lima, P. J. F. (1996). 11 Nonlinear time series, complexity theory, and finance. In Handbook of Statistics (Vol. 14, pp. 317–361). https://doi.org/10.1016/S0169-7161(96)14013-X
Dias, R., & Pereira, J. M. (2021). The Impact of the COVID-19 Pandemic on Stock Markets. International Journal of Entrepreneurship and Governance in Cognitive Cities, 1(2), 57–70. https://doi.org/10.4018/ijegcc.2020070105
Dias, R., Pereira, J. M., & Carvalho, L. C. (2022). Are African Stock Markets Efficient? A Comparative Analysis Between Six African Markets, the UK, Japan and the USA in the Period of the Pandemic. Naše Gospodarstvo/Our Economy, 68(1), 35–51. https://doi.org/10.2478/ngoe-2022-0004
Dias, R., Santos, H., Heliodoro, P., Vasco, C., & Alexandre, P. (2021). Wti Oil Shocks in Eastern European Stock Markets: a Var Approach. 5th EMAN Conference Proceedings (Part of EMAN Conference Collection), October, 71–84. https://doi.org/10.31410/eman.2021.71
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987. https://doi.org/10.2307/1912773
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3–25. https://doi.org/10.1016/0304-405X(88)90020-7
Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
Karasiński, J. (2020). The Changing Efficiency of the European Stock Markets. Annales Universitatis Mariae Curie-Skłodowska, Sectio H – Oeconomia. https://doi.org/10.17951/h.2020.54.1.41-51
Lawrence H. Summers. (1986). Does the stock market rationally reflect fundamental values. The Journal of Finance. https://doi.org/10.2307/2328487
Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics. https://doi.org/10.1016/S0304-4076(01)00098-7
Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303. https://doi.org/10.1093/biomet/65.2.297
Poterba, J. M., & Summers, L. H. (1988). Mean reversion in stock prices. Evidence and Implications. Journal of Financial Economics. https://doi.org/10.1016/0304-405X(88)90021-9
Rehman, S., Chhapra, I. U., Kashif, M., & Rehan, R. (2018). Are Stock Prices a Random Walk? An Empirical Evidence of Asian Stock Markets. ETIKONOMI. https://doi.org/10.15408/etk.v17i2.7102
Taylor, S. J. (1986). Modelling Financial Time Series. In Wiley New York.
Vasco, C., Pardal, P., & Dias, R. T. (2021). Do the Stock Market Indices Follow a Random Walk? May, 389–410. https://doi.org/10.4018/978-1-7998-6643-5.ch022
Zebende, G. F., Santos Dias, R. M. T., & de Aguiar, L. C. (2022). Stock market efficiency: An intraday case of study about the G-20 group. Heliyon, 8(1), e08808. https://doi.org/10.1016/j.heliyon.2022.e08808
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