Risk spillovers between the Vietnamese dong and key Asian currencies before and during the Covid-19 pandemic
PDF (Vietnamese)

Keywords

risk spillovers
exchange rate
Covid-19 pandemic
VND lây lan rủi ro
tỷ giá
dịch Covid-19
VND

Abstract

This research utilizes the framework of forecast error variance decomposition to examine the extent of risk spillovers between the Vietnamese dong (VND) and vital Asian currencies before and during the Covid-19 pandemic. The findings show that, in general, the risk contagion between the VND and other crucial Asian currencies in the study is modest. Second, the intensity of risk spillovers is not constant but varies over time, spiking significantly when Covid-19 became a pandemic. Third, the VND is a net-risk receiver currency, especially from stronger currencies such as KRW, SGD, or JPY, and becomes more vulnerable during the disease occurrence.

https://doi.org/10.26459/hueunijed.v133i5A.7325
PDF (Vietnamese)

References

  1. Diebold, F. X. and Yılmaz, K. (2015), Financial and macroeconomic connectedness: A network approach to measurement and monitoring, Oxford University Press, USA.
  2. Diebold, F. X. and Yilmaz, K. (2012), Better to give than to receive: Predictive directional measurement of volatility spillovers, International Journal of Forecasting, 28(1), 57–66, doi: 10.1016/j.ijforecast.2011.02.006.
  3. Alfani, G. and Percoco, M. (2019), Plague and long‐term development: the lasting effects of the 1629–30 epidemic on the Italian cities, The Economic History Review, 72(4), 1175–1201, doi: 10.1111/ehr.12652.
  4. McLafferty, S. (2010), Placing Pandemics: Geographical Dimensions of Vulnerability and Spread, Eurasian Geography and Economics, 51(2), 143–161, doi: 10.2747/1539-7216.51.2.143.
  5. Dauda, R. S. (2019), HIV/AIDS and economic growth: Evidence from West Africa, Health Planning & Management, 34(1), 324–337, doi: 10.1002/hpm.2633.
  6. Drali, R., Brouqui, P., and Raoult, D. (2014), Typhus in world war I, Microbiology Today, 41(2), 58–61.
  7. Nor, N. M., Sirag, A., Thinng, W. B. K., and Waziri, S. I. (2015), Diseases and economic performance: Evidence from panel data, Asian Social Science, 11(9), 198.
  8. International Monetary Fund (2022), Annual Report on Exchange Arrangements and Exchange Restrictions 2021, in Annual Report on Exchange Arrangements and Exchange Restrictions 2021. Accessed: Oct. 30, 2023. [Online]. Available: https://www.elibrary.imf.org/display/book/9781513598956/9781513598956.xml.
  9. Louhichi, W., Ftiti, Z., and Ameur, H. B. (2021), Measuring the global economic impact of the coronavirus outbreak: Evidence from the main cluster countries, Technological Forecasting and Social Change, 167, 120732, doi: 10.1016/j.techfore.2021.120732.
  10. Vidya, C. T. and Prabheesh, K. P. (2020), Implications of COVID-19 Pandemic on the Global Trade Networks, Emerging Markets Finance and Trade, 56(10), 2408–2421, doi: 10.1080/1540496X.2020.1785426.
  11. Nepp, A., Okhrin, O., Egorova, Dzhuraeva, J. Z. and Zykov, A. (2022), What threatens stock markets more - The coronavirus or the hype around it?, International Review of Economics & Finance, 78, 519–539, doi: 10.1016/j.iref.2021.12.007.
  12. Nguyen T. H., Nguyen, L. T. M. and Vo, X. V. (2022), Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and Transfer Entropy approaches, Journal of International Financial Markets, Institutions and Money, 81, 101628, doi: 10.1016/j.intfin.2022.101628.
  13. Narayan P. K. (2022), Understanding exchange rate shocks during COVID-19, Finance Research Letters, 45, 102181.
  14. Mo, W. S., Yang, J. J. and Chen, Y. L. (2023), Exchange rate spillover, carry trades, and the COVID-19 pandemic, Economic modelling, 121, 106222.
  15. Lu, C., Li, J. L. Liu, and Yu, F. (2023), Spillover effect of the RMB and Non-USD currencies after the COVID-19 pandemic: Evidence captured from 30-minute high frequency data, International Review of Economics & Finance, 84, 527–552.
  16. Wei, Z., Luo, Y., Huang, Z. and Guo, K. (2020), Spillover effects of RMB exchange rate among B&R countries: Before and during COVID-19 event, Finance research letters, 37, 101782.
  17. Hällström, M. (2020), Financial connectedness of the Nordic banking sector: examining time and frequency connectedness in return volatilities of bank shares.
  18. Diebold F. X. and Yilmaz, K. (2009), Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets, The Economic Journal, 119(534), 158–171, doi: 10.1111/j.1468-0297.2008.02208.x.
  19. Jang, W., Lee, J. and Chang, W. (2011), Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree, Physica A: Statistical Mechanics and its Applications, 390(4), 707–718, doi: 10.1016/j.physa.2010.10.028.
  20. Bubák, V., Kočenda, E. and F. Žikeš (2011), Volatility transmission in emerging European foreign exchange markets, Journal of Banking & Finance, 35(11), 2829–2841, doi: 10.1016/j.jbankfin.2011.03.012.
  21. Greenwood-Nimmo, M., Nguyen, V. H. and Rafferty, B. (2016), Risk and return spillovers among the G10 currencies, Journal of Financial Markets, 31(43–62), doi: 10.1016/j.finmar.2016.05.001.
  22. Baruník, J., Kočenda, E. and Vácha, L. (2017), Asymmetric volatility connectedness on the forex market, Journal of International Money and Finance, 77(39–56), doi: 10.1016/j.jimonfin.2017.06.003.
  23. Kočenda, E. and Moravcová, M. (2019), Exchange rate comovements, hedging and volatility spillovers on new EU forex markets, Journal of International Financial Markets, Institutions and Money, 58, 42–64, doi: 10.1016/j.intfin.2018.09.009.
  24. Mohammed, W. A. (2021), Volatility Spillovers among Developed and Developing Countries: The Global Foreign Exchange Markets, Journal of Risk and Financial Management, 14(6), 270.
  25. Wen, T. and Wang, G. J. (2020), Volatility connectedness in global foreign exchange markets, Journal of Multinational Financial Management, 54, 100617.
  26. Xu, X., Wu, S. and Wu, Y. (2015), The relationship between Renminbi’s exchange rate and East Asia currencies before and after the financial crisis, China Finance Review International, 5(1), 34–52.
  27. Fasanya, I. O., Oyewole, O., Adekoya, O. B. and Odei-Mensah, J. (2021), Dynamic spillovers and connectedness between COVID-19 pandemic and global foreign exchange markets, Economic Research-Ekonomska Istraživanja, 34(1), 2059–2084.
  28. BIS, (2019), Triennial Central Bank Survey of foreign exchange and OTC derivatives markets in 2019, Accessed: Oct. 10, 2022. [Online]. Available: https://www.bis.org/publ/rpfx16.htm.
  29. BIS, (2022), Triennial Central Bank Survey OTC foreign exchange Turnover in April 2022. Bank for International Settlement, Accessed: Oct. 10, 2022. [Online]. Available: https://www.bis.org/statistics/rpfx19.htm
  30. Umar, Z., Jareño, F. and González, M. de la O. (2021), The impact of COVID-19-related media coverage on the return and volatility connectedness of cryptocurrencies and fiat currencies, Technological Forecasting and Social Change, 172, 121025, doi: 10.1016/j.techfore.2021.121025.
  31. Parkinson, M. (1980), The extreme value method for estimating the variance of the rate of return, Journal of business, 61–65.
  32. Koop, G. Pesaran, M. H. and Potter, S. M. (1996), Impulse response analysis in nonlinear multivariate models, Journal of econometrics, 74(1), 119–147.
  33. Pesaran, H. H. and Shin, Y. (1998), Generalized impulse response analysis in linear multivariate models, Economics Letters, 58(1), 17–29, doi: 10.1016/S0165-1765(97)00214-0.
  34. Wikipedia (2023), History of COVID-19 vaccine development - Wikipedia, Accessed: Mar. 09, 2023. [Online]. Available: https://en.wikipedia.org/wiki/History_of_COVID-19_vaccine_development
  35. Martin, F. E. et al. (2020), Capital flows during the pandemic: lessons for a more resilient international financial architecture, Bank of Italy Occasional Paper, 589.
  36. De Bondt, W. F. M. and Thaler, R. H. (1987), Further Evidence On Investor Overreaction and Stock Market Seasonality, The Journal of Finance, 42(3), 557–581, doi: 10.1111/j.1540-6261.1987.tb04569.x.
  37. DellaVigna, S. (2009), Psychology and economics: Evidence from the field, Journal of Economic literature, 47(2), 315–372.
  38. Boyer, B. H., Kumagai, T. and Yuan, K. (2006), How Do Crises Spread? Evidence from Accessible and Inaccessible Stock Indices, The Journal of Finance, 61(2), 957–1003, doi: 10.1111/j.1540-6261.2006.00860.x.
  39. Gehlen, F. L. (1977), Toward a revised theory of hysterical contagion, Journal of Health and Social Behavior, 27–35.
  40. Huis In ‘T Veld, E. M. J. and De Gelder, B. (2015), From personal fear to mass panic: The neurological basis of crowd perception, Human Brain Mapping, 36(6), 2338–2351, doi: 10.1002/hbm.22774.
  41. Mawson, A. R. (2005), Understanding Mass Panic and Other Collective Responses to Threat and Disaster, Psychiatry: Interpersonal and Biological Processes, 68(2), 95–113, doi: 10.1521/psyc.2005.68.2.95.
  42. Kyle A. S. and Xiong, W. (2001), Contagion as a Wealth Effect, The Journal of Finance, 56(4), 1401–1440, doi: 10.1111/0022-1082.00373.
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2024 Array