Spillovers across global stock markets before and after the declaration of Russia’s invasion of Ukraine
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DOIhttp://dx.doi.org/10.21511/imfi.21(2).2024.10
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Article InfoVolume 21 2024, Issue #2, pp. 130-143
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Since the financial meltdown, studies on systemic risk and financial contagion have gained currency. Events like the COVID pandemic and the Russian invasion of Ukraine have fueled such an importance. This study examines the impact of the invasion on volatility transmissions across major stock markets worldwide. The stock indices considered in this study are ASX 200, ESTOXX 40, FTSE 100, HNGSNG, NIFTY 50, NIKKIE, and S&P 500. The work uses Vector Auto Regression (VAR) to study the transmission of returns. Later, the work performs Dynamic Conditional Covariance-Generalized Auto Regression Conditional Heteroskedasticity (DCC-GARCH) on the residuals where the transmission of returns was significant. The DCC-GARCH (E-GARCH) shows that all the asymmetric transmissions are negative. The study finds that co-movements of stock returns for the following pairs: ESTOXX 50-S&P 500, NIFTY 50-FTSE100, NIFTY 50-NIKKIE, NIKKIE-ESTOXX 50, S&P 500-NIFTY 50, and SP500-HNGSNG significantly intensified after the declaration of invasion. Such intensification of co-movements does establish the contagion effect triggered by invasion. The study shows that ESTOXX 50, which has the closest geographical proximity to the war zone, happens to be the highest generator of spillovers.
- Keywords
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JEL Classification (Paper profile tab)G15, G12, G11
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References43
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Tables6
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Figures2
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- Figure 1. VAR residuals of stock market returns
- Figure 2. Dynamic conditional correlations for significant transmission
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- Table 1. Descriptive statistics
- Table 2. Vector Auto Regression results on stock returns
- Table 3. Autocorrelation and GARCH test results of spillovers
- Table 4. Spillover results of DCC-GARCH (S-GARCH)
- Table 5. Spillover results of DCC-GARCH (E-GARCH)
- Table 6. Spillover results of DCC-GARCH (S-GARCH)
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- Abbas, Q., Khan, S., & Shah, S. Z. A. (2013). Volatility transmission in regional Asian stock markets. Emerging Markets Review, 16, 66-77.
- Ackermann, F., Eden, C., Williams, T., & Howick, S. (2007). Systemic risk assessment: a case study. Journal of the Operational Research Society, 58, 39-51.
- Antoniou, A., Pescetto, G. M., & Stevens, I. (2007). Market-wide and sectoral integration: evidence from the UK, USA and Europe. Managerial Finance, 33(3), 173-194.
- Alexandridis, A. K., & Hasan, M. S. (2020). Global financial crisis and multiscale systematic risk: Evidence from selected European stock markets. International Journal of Finance & Economics, 25(4), 518-546.
- Apergis, N., & Apergis, E. (2022). The role of Covid-19 for Chinese stock returns: evidence from a GARCHX model. Asia-Pacific Journal of Accounting & Economics, 29(5), 1175-1183.
- Baruník, J., Kočenda, E., & Vácha, L. (2016). Asymmetric connectedness on the US stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27, 55-78.
- Battiston, S., Caldarelli, G., May, R. M., Roukny, T., & Stiglitz, J. E. (2016). The price of complexity in financial networks. Proceedings of the National Academy of Sciences, 113(36), 10031-10036.
- Billio, M., & Caporin, M. (2010). Market linkages, variance spillovers, and correlation stability: Empirical evidence of financial contagion. Computational Statistics & Data Analysis, 54(11), 2443-2458.
- Bohl, M. T., Siklos, P. L., & Sondermann, D. (2008). European stock markets and the ECB’s monetary policy surprises. International Finance, 11(2), 117-130.
- Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. The Review of Economics and Statistics, 498-505.
- Bordo, M. D., & Murshid, A. P. (2001). Are financial crises becoming more contagious?: What is the historical evidence on contagion? In International Financial Contagion (pp. 367-403). Boston, MA: Springer US.
- Centeno, M. A., Nag, M., Patterson, T. S., Shaver, A., & Windawi, A. J. (2015). The emergence of global systemic risk. Annual Review of Sociology, 41, 65-85.
- Chancharat, S., & Sinlapates, P. (2023). Dependences and dynamic spillovers across the crude oil and stock markets throughout the COVID-19 pandemic and Russia-Ukraine conflict: Evidence from the ASEAN+ 6. Finance Research Letters, 57, 104249.
- Chen, J., & Zhang, J. (2023). Crude oil price shocks, volatility spillovers, and global systemic financial risk transmission mechanisms: Evidence from the stock and foreign exchange markets. Resources Policy, 85, 103875.
- Choi, S. Y. (2022). Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday. The North American Journal of Economics and Finance, 59, 101614.
- Costola, M., & Lorusso, M. (2022). Spillovers among energy commodities and the Russian stock market. Journal of Commodity Markets, 28(C), 100249.
- Cotter, J., & Suurlaht, A. (2019). Spillovers in risk of financial institutions. The European Journal of Finance, 25(17), 1765-1792.
- 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.
- Elsayed, A. H., Gozgor, G., & Lau, C. K. M. (2022). Risk transmissions between bitcoin and traditional financial assets during the COVID-19 era: The role of global uncertainties. International Review of Financial Analysis, 81, 102069.
- Engle, R., Ito, T., & Lin, W. L. (1990). Meteor Showers or Heat Waves? Heteroskedastic Intra-daily Volatility in the Foreign Exchange Market. Econometrica, 58(3), 525-42.
- Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics, 22(4), 367-381.
- Engle, R. F., & Sheppard, K. K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH (No. qt5s2218dp). UC San Diego: Department of Economics.
- Federle, J., Meier, A., & Sehn, V. (2022). Proximity to War: The stock market response to the Russian invasion of Ukraine (No. 17185) (CEPR Discussion Papers).
- Gang, J., Ye, N. and Zhang, C., 2012. Financial Crisis, Risk Perception and the Implied Volatility Transmission: A Cross-Region Study. The Manchester School, 80, 92-120.
- Gao, J., Zhu, S., O’Sullivan, N., & Sherman, M. (2019). The role of economic uncertainty in UK stock returns. Journal of Risk and Financial Management, 12(1), 5.
- Gibson, R., & Mougeot, N. (2004). The pricing of systematic liquidity risk: Empirical evidence from the US stock market. Journal of Banking & Finance, 28(1), 157-178.
- Glasserman, P., & Young, H. P. (2016). Contagion in financial networks. Journal of Economic Literature, 54(3), 779-831.
- Jammazi, R., Ferrer, R., Jareño, F., & Shahzad, S. J. H. (2017). Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective? International Review of Economics & Finance, 49, 453-483.
- Kocaarslan, B., Soytas, U., Sari, R., & Ugurlu, E. (2019). The changing role of financial stress, oil price, and gold price in financial contagion among US and BRIC markets. International Review of Finance, 19(3), 541-574.
- Louati, A., & Firano, Z. (2022). COVID-19 and cross-border contagion: Trade and financial flows. Research in Globalization, 4, 100082.
- Li, W. (2021). COVID-19 and asymmetric volatility spillovers across global stock markets. The North American Journal of Economics and Finance, 58, 101474.
- Mittal, A., Sehgal, S., & Mittal, A. (2019). Dynamic currency linkages between select emerging market economies: An empirical study. Cogent Economics & Finance, 7(1), 1681581.
- Nguyen, M. T. N. (2023). Examining contagion effects between global crude oil prices and the Southeast Asian stock markets during the COVID-19 pandemic. Investment Management and Financial Innovations, 20(1), 77-87.
- Pauly, L. W. (2008). Financial crisis management in Europe and beyond. Contributions to Political Economy, 27(1), 73-89.
- Raavinuthala, S. K. S., Jain, G., & Biswal, P. C. (2023). Internecine interrelations among liquidity risk, market risk and credit risk in Indian banking system. Afro-Asian Journal of Finance and Accounting, 13(6), 780-797.
- Singhal, S., & Ghosh, S. (2016). Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models. Resources Policy, 50, 276-288.
- Smales, L. A. (2017). Effect of investor fear on Australian financial markets. Applied Economics Letters, 24(16), 1148-1153.
- Umar, Z., Polat, O., Choi, S. Y., & Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Finance Research Letters, 48, 102976.
- Yang, K., Kim, M. H., & Kim, Y. M. (2019). Financial connectedness revisited: the role of Fama-French risk factors. Applied Economics Letters, 26(10), 850-856.
- Zeng, H., & Lu, R. (2022). High-frequency volatility connectedness and time-frequency correlation among Chinese stock and major commodity markets around COVID-19. Investment Management and Financial Innovations, 19(2), 260-273.
- Zhao, X., Zhang, W. G., & Liu, Y. J. (2020). Volatility spillovers and risk contagion paths with capital flows across multiple financial markets in China. Emerging Markets Finance and Trade, 56(4), 731-749.
- Zhu, B., Lin, R., Deng, Y., Chen, P., & Chevallier, J. (2021). Intersectoral systemic risk spillovers between energy and agriculture under the financial and COVID-19 crises. Economic Modelling, 105, 105651.
- Zihui, Y., & Yinggang, Z. (2020). Global systemic financial risk spillovers and their external shocks. Social Sciences in China, 41(2), 26-49.