Connectedness of Vietnamese bank stock returns under the impact of the COVID-19 pandemic
-
DOIhttp://dx.doi.org/10.21511/bbs.18(4).2023.18
-
Article InfoVolume 18 2023, Issue #4, pp. 209-225
- 233 Views
-
92 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The COVID-19 pandemic highlighted the sensitivity of connectedness among bank stock returns in Vietnam. The aim of this study is to examine the strength of this connectedness along with the effect of government lockdown policy and COVID-19 cases on the total connectedness index (TCI) of 16 listed banks on Vietnamese stock exchanges. They are assessed using the database of FiinPro on the banking sector between January 2020 and July 2022, Vietnam Center for Disease Control and Prevention (CDC), and The World Health Organization (WHO) on the COVID-19 pandemic, employing a time-varying-parameter vector autoregressive (TVP-VAR) connectedness framework and the conditional quantile regression model.
The results show that at the firm level, there is strong interdependence among bank stock returns with the average TCI being as high as 90.66%. It is also revealed that medium and large-sized banks are receivers of shock, while smaller banks are transmitters. As far as the impact on TCI is concerned, the widespread of the pandemic with the increasing number of COVID-19 cases is significantly negative, whereas the tightening of lockdown is significantly positive. Besides, the degree of the impact varies according to the 95th, 75th, 50th and 25th levels of conditional quantile regression. Based on the study’s findings, individual investors are recommended to thoroughly analyze the connectedness of banks before making investment decisions, while bank regulators should strengthen controls on credit relationships with small banks. Regarding policy makers, it is proposed to apply flexible restrictions and short-term lockdown depending on the actual outbreak of the pandemic.
Acknowledgment
The paper was conducted within the scope of Project QG21.48 of Vietnam National University.
- Keywords
-
JEL Classification (Paper profile tab)G01, G10, C23
-
References50
-
Tables7
-
Figures3
-
- Figure 1. Connectedness network of stock returns of 16 Vietnamese banks
- Figure 2. Total dynamic connectedness of 16 bank stock returns
- Figure 3. Change of net directional connectedness of individual bank stock returns in the study period
-
- Table 1. Variables used in regression models
- Table 2. Summary statistics of bank stock returns and COVID-19 cases
- Table 3. The average connectedness measures of individual bank stock returns for the study period
- Table 4. Impact of the number of COVID-19 cases on TCI-linear regression and quantile regression
- Table A1. Pairwise correlation among bank stock returns
- Table B1. Movements of bank stock returns and the COVID-19 cases
- Table C1. Linear regression (LR) – impact of the pandemic on the NET connectedness values of listed banks (xth quantile of the NET)
-
- Ackert, L. F., & Deaves, R. (2010). Behavioral finance: psychology, decision-making, and markets. South-Western Cengage Learning.
- Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1705-1741.
- Akyildirim, E., Cepni, O., Molnár, P., & Uddin, G. S. (2022). Connectedness of energy markets around the world during the COVID-19 pandemic. Energy Economics, 109, 105900.
- Alaganar, V. T., & Bhar, R. (2002). Information and volatility linkage under external shocks: Evidence from dually listed Australian stocks. International Review of Financial Analysis, 11(1), 59-71.
- Aldasoro, I., Huang, W., & Kemp, E. (2020). Cross-border links between banks and non-bank financial institutions. BIS Quarterly Review, 61-74.
- Alexakis, C., Eleftheriou, K., & Patsoulis, P. (2021). COVID-19 containment measures and stock market returns: An international spatial econometrics investigation. Journal of Behavioral and Experimental Finance, 29, 100428.
- Aliani, K., Al-kayed, L., & Boujlil, R. (2022). COVID-19 effect on Islamic vs. conventional banks’ stock prices: Case of GCC countries. The Journal of Economic Asymmetries, 26, e00263.
- Antonakakis, N., & Gabauer, D. (2017). Refined Measures of Dynamic Connectedness based on TVP-VAR (MPRA Paper No. 78282). University Library of Munich, Germany.
- Apostolakis, G. N., Floros, C., & Giannellis, N. (2022). On bank return and volatility spillovers: Identifying transmitters and receivers during crisis periods. International Review of Economics & Finance, 82, 156-176.
- 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, 38, 101701.
- Bisias, D., Flood, M., Lo, A. W., & Valavanis, S. (2012). A Survey of Systemic Risk Analytics. Annual Review of Financial Economics, 4, 255-296.
- Bouri, E., Cepni, O., Gabauer, D., & Gupta, R. (2021). Return connectedness across asset classes around the COVID-19 outbreak. International Review of Financial Analysis, 73, 101646.
- Caporale, G. M., Kang, W. Y., Spagnolo, F., & Spagnolo, N. (2022). The COVID-19 pandemic, policy responses and stock markets in the G20. International Economics, 172, 77-90.
- Chen, Y., Hu, J., & Zhang, W. (2020). Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network. China & World Economy, 28(6), 78-100.
- Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
- Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33(1), 1-15.
- Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(534), 158-171.
- 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.
- Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134.
- Dong, Z., Li, Y., Zhuang, X., & Wang, J. (2022). Impacts of COVID-19 on global stock sectors: Evidence from time-varying connectedness and asymmetric nexus analysis. The North American Journal of Economics and Finance, 62, 101753.
- FiinPro (2022). FiinPro Database.
- Foglia, M., & Angelini, E. (2020). From me to you: Measuring connectedness between Eurozone financial institutions. Research in International Business and Finance, 54, 101238.
- FTSE Russell. (2022). FTSE Equity Country Classification September 2022 Annual Announcement.
- Greene, E. F., McIlwain, K. L., & Scott, J. T. (2010). A closer look at ‘too big to fail’: national and international approaches to addressing the risks of large, interconnected financial institutions. Capital Markets Law Journal, 5(2), 117-140.
- Hanif, W., Mensi, W., & Vo, X. V. (2021). Impacts of COVID-19 outbreak on the spillovers between US and Chinese stock sectors. Finance Research Letters, 40, 101922.
- Hernandez, J. A., Kang, S. H., Shahzad, S. J. H., & Yoon, S. M. (2020). Spillovers and diversification potential of bank equity returns from developed and emerging America. The North American Journal of Economics and Finance, 54, 101219.
- Kassamany, T., & Zgheib, B. (2023). Impact of government policy responses of COVID-19 pandemic on stock market liquidity for Australian companies. Australian Economic Papers, 62(1), 24-46.
- Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E. (2010). Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market. PLoS ONE, 5(12), 15032.
- Khalfaoui, R., Mefteh-Wali, S., Dogan, B., & Ghosh, S. (2023). Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis. International Review of Financial Analysis, 86, 102496.
- Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101-116.
- Luong, A. T., Thanh, T. Le, & Phung, H. T. T. (2022). Stabilize market sentiment to protect investors in the Vietnamese stock market. 2022 IAFICO Annual Conference: Global Forum Financial Consumers. Financial Consumer Protection and Sustainable Development (pp. 181-197).
- McGinnis, P. J. (2004). Social Theory at HBS: McGinnis’ Two FOs. The Harbus.
- Mensi, W., Vo, X. V., Ko, H. U., & Kang, S. H. (2023). Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis. Economic Analysis and Policy, 77, 558-580.
- Niţoi, M., & Pochea, M. M. (2022). The nexus between bank connectedness and investors’ sentiment. Finance Research Letters, 44, 102432.
- Onnela, J.-P., Chakraborti, A., Kaski, K., Kertesz, J., & Kanto, A. (2003). Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review E, 68(5).
- Ouyang, Z., Chen, S., Lai, Y., & Yang, X. (2022). The correlations among COVID-19, the effect of public opinion, and the systemic risks of China’s financial industries. Physica A: Statistical Mechanics and Its Applications, 600, 127518.
- Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29.
- Przybylski, A. K., Murayama, K., Dehaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841-1848.
- Qian, B., Wang, G. J., Feng, Y., & Xie, C. (2022). Partial cross-quantilogram networks: Measuring quantile connectedness of financial institutions. The North American Journal of Economics and Finance, 60, 101645.
- Razmi, S. F., & Razmi, S. M. J. (2023). The role of stock markets in the US, Europe, and China on oil prices before and after the COVID-19 announcement. Resources Policy, 81, 103386.
- Rehman, M. U., Ahmad, N., Shahzad, S. J. H., & Vo, X. V. (2022). Dependence dynamics of stock markets during COVID-19. Emerging Markets Review, 51, 100894.
- Rizwan, M. S., Ahmad, G., & Ashraf, D. (2022). Systemic risk, Islamic banks, and the COVID-19 pandemic: An empirical investigation. Emerging Markets Review, 51, 100890.
- Shahzad, S. J. H., Bouri, E., Kristoufek, L., & Saeed, T. (2021). Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers. Financial Innovation, 7(1), 14.
- Song, P., Zhang, X., Zhao, Y., & Xu, L. (2020). Exogenous Shocks on the Dual-country Industrial Network: A Simulation Based on the Policies during the COVID-19 Pandemic. Emerging Markets Finance and Trade, 56(15), 3554-3561.
- Tabak, B. M., Silva, I. B. D. R. e., & Silva, T. C. (2022). Analysis of connectivity between the world’s banking markets: The COVID-19 global pandemic shock. The Quarterly Review of Economics and Finance, 84, 324-336.
- Tran, N., & Uzmanoglu, C. (2023). Reprint of: COVID-19, lockdowns, and the municipal bond market. Journal of Banking & Finance, 147, 106758.
- Uddin, G. S., Yahya, M., Goswami, G. G., Lucey, B., & Ahmed, A. (2022). Stock market contagion during the COVID-19 pandemic in emerging economies. International Review of Economics & Finance, 79, 302-309.
- Vietnamese Government. (2021). Temporary regulations “Safely adapting, flexibly, effectively controlling the Covid-19 epidemic” (128/NQ-CP). Vietnamese Government. (In Vietnamese).
- Wang, G. J., Xie, C., Zhao, L., & Jiang, Z. Q. (2018). Volatility connectedness in the Chinese banking system: Do state-owned commercial banks contribute more? Journal of International Financial Markets, Institutions and Money, 57, 205-230.
- Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36, 101528.