Stock crash and how it spreads over the Vietnamese stock market


Authors

  • Vu Thi Loan VNU University of Economics and Business
  • Luong Ngoc Hai VNU University of Economics and Business
  • Nguyen Phuong Nga VNU University of Economics and Business
DOI: https://doi.org/10.57110/vnu-jeb.v4i3.339

Keywords:

Stock crash, spillover mechanism, industry stock returns, TVP-VAR model

Abstract

This study explores the spillover mechanism and impact of stock crashes on the Vietnamese stock market from 2007 to 2022 with a total of 669.452 observations. The research aims to clarify how crashes propagate across sectors and their broader market implication. Findings show that stock crashes not only affect their originating industry but also spread to others, triggering a ripple effect throughout the market. Employing the autoregressive vector regression model with time-varying parameters (TVP-VAR), the study underscores the pivotal role of industries such as minerals and real estate in transmitting crash effects. The insights contribute to a deeper understanding of the sectoral interdependence within the stock market, assisting in formulation of robust investment and risk management strategies.

References

Andreou, P. C., Magidou, M. & Lambertides, N. (2021). Stock price crash risk: A critique of the agency theory viewpoint. Review of Corporate Finance Studies, 10(1),134-165. http://doi.org/10.2139/ssrn.3774424

Antonakakis, N., Gabauer, D., Gupta, R., & Plakandaras, V. (2018). Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics Letters, 166, 63-75. https://doi.org/10.1016/j.econlet.2018.02.011

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2019). Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios. Journal of International Financial Markets, Institutions and Money, 61, 37-51. https://doi.org/10.1016/j.intfin.2019.02.003

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/jrfm13040084

Claessens, S., Kose, M. A. & Terrones, M. E. (2012). The global financial crisis: How similar? How different? How costly? Journal of International Money and Finance, 31(3), 671-688. https://doi.org/10.2139/ssrn.1573958

Dang, T. H. N., Nguyen, N. T. & Vo, D. H. (2022). Sectoral volatility spillovers and their determinants in Vietnam. Economic Change and Restructuring, 56, 681–700. https://doi.org/10.1007/s10644-022-09446-9

Diebold, F. X., Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119, 158–171. https://doi.org/10.3386/w13811

Diebold, F. X., Yilmaz, K., (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28, 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006

Diebold, F. X., Yilmaz, K., (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182,119–134. https://doi.org/10.1016/j.jeconom.2014.04.012

Hutton, A. P., Marcus, A. J., & Tehranian, H. (2009). Opaque financial reports, R2, and crash risk. Journal of Financial Economics, 94(1), 67-86. https://doi.org/10.1016/j.jfineco.2008.10.003

Jin, L., Myers, & S. C. (2006). R2 around the world: New theory and new tests. Journal of Financial Economics, 79(2), 257-292. https://doi.org/10.1016/j.jfineco.2004.11.003

Mendoza, E. G. & Terrones, M. E. (2008). An anatomy of credit booms: Evidence from macro aggregates and micro data. Journal of International Economics, 76(1), 105-130. https://doi.org/10.3386/w14049

Nguyen, T. D., Dinh, H. T. & Vo, D. T., (2019). Financial contagion in emerging markets: Evidence from the COVID-19 pandemic. Emerging Markets Review, 41, 100-660.

Shen, Y. Y., Jiang, Z. Q., Ma, J. C., Wang, G. J., & Zhou, W.X. (2022). Sector connectedness in the Chinese stock markets. Empir Econ, 62(2), 825–852. https://doi.org/10.1007/s00181-021-02036-0

Yin, K., Liu, Z., Jin, X. (2020). Interindustry volatility spillover effects in China’s stock market. Phys A Stat Mech Appl, 539, 122936. https://doi.org/10.1016/j.physa.2019.122936

Downloads

Download data is not yet available.

Additional Files

Published

25-06-2024

Abstract View

421

PDF Downloaded

0

How to Cite

Vu Thi Loan, Luong Ngoc Hai, & Nguyen Phuong Nga. (2024). Stock crash and how it spreads over the Vietnamese stock market. VNU JOURNAL OF ECONOMICS AND BUSINESS, 4(3), 107. https://doi.org/10.57110/vnu-jeb.v4i3.339

Issue

Section

Original Article