1. Home
  2. Volume 7 - Number 3
  3. An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic
New submission
Issues
Journal Information
Aims and ScopeEditorial BoardEditorial Advisory BoardJournal Policies
Resources
For Readers
For Authors
For Editors
For Reviewers
Publication EthicsCall for Special Issue
ISSN
ISSN 2615-9821
ISSN(Previous) 2588-1396

Volume 7 - Number 3 | November 2023

An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic

Ornanong Puarattanaarunkorn, Kittawit Autchariyapanitkul, and Teera Kiatmanaroch

25/11/2023 123 119 0
Like Buy now 20.000 đ
Stock markets QE GARCH Copula Network analysis COVID-19 pandemic

Abstract:

Purpose
Unlimited quantitative easing (QE) is one of the monetary policies used to stimulate the economy during the coronavirus disease 2019 (COVID-19) pandemic. This policy has affected the financial markets worldwide. This empirical research aims at studying the dependence among stock markets before and after unlimited QE announcements.

Design/methodology/approach
The copula-based GARCH (1,1) and minimum spanning tree models are used in this study to analyze 14 series of stock market data, on 6 ASEAN and 8 other countries outside the region. The data are divided into two periods to compare the differences in dependence.

Findings
The findings show changes in dependence among the volatility of daily returns in 14 stock markets during each period. After the unlimited QE announcement, the upper tail dependence became more apparent, while the role of the lower tail dependence was reduced. The minimum spanning tree can show the close relationships between stock markets, indicating changes in the connection network after the announcement.

Originality/value
This study allows the dependency to be compared between stock market volatility before and after the announcement of unlimited QE during the COVID-19 pandemic. Moreover, the study fills the literature gap by combining the copula-based GARCH and the minimum spanning tree models to analyze and reveal the systemic network of the relationships.

References:

  1. Akaike, H. (1973), “Information theory and an extension of the maximum likelihood principle”, in Petrov, B.N. and Csaki, F. (Eds), Proceedings of the 2nd International Symposium on Information Theory, Akademiai Kiado, Budapest, pp. 267-281.
  2. Aloui, R., Aïssa, M.S.B. and Nguyen, D.K. (2013), “Conditional dependence structure between oil prices and exchange rates: a copula-GARCH approach”, Journal of International Money and Finance, Vol. 32 No. 1, pp. 719-738, doi: 10.1016/j.jimonfin.2012.06.006.
  3. ASEAN Secretariat (2016), “Regional comprehensive economic partnership (RCEP)”, ASEAN Secretariat. https://asean.org/regional-comprehensive-economic-partnership-rcep/ (Accessed 17 December 2022).
  4. ASEAN Secretariat (2020), “ASEAN policy brief: economic impact of COVID-19 outbreak on ASEAN”, available at: https://asean.org/book/1st-asean-policy-brief-economic-impact-of-covid-19-outbreak-on-asean/ (accessed 10 December 2020).
  5. Aslam, F., Mohmand, Y.T., Ferreira, P., Memon, B.A., Khan, M. and Khan, M. (2020), “Network analysis of global stock markets at the beginning of the coronavirus disease (Covid-19) outbreak”, Borsa Istanbul Review, Vol. 20 No. 1, pp. S49-S61, doi: 10.1016/j.bir.2020.09.003.
  6. Aswani, J. (2017), “Impact of global financial crisis on network of Asian stock markets”, Algorithmic Finance, Vol. 6 Nos 3-4, pp. 79-91, doi: 10.3233/af-170192.
  7. Beirne, J., Renzhi, N., Sugandi, E. and Volz, U. (2020), “Financial market and capital flow dynamics during the COVID-19 pandemic”, ADBI Working Paper No.1158, Asian Development Bank Institute, Tokyo, June 2020.
  8. Bollerslev, T. (1986), “Generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, Vol. 31 No. 3, pp. 307-327, doi: 10.1016/0304-4076(86)90063-1.
  9. Bollerslev, T., Chou, R.Y. and Kroner, K. (1992), “ARCH modeling in finance: a review of the theory and empirical evidence”, Journal of Econometrics, Vol. 52 Nos 1-2, pp. 5-59, doi: 10.1016/0304-4076(92)90064-x.
  10. Brechmann, E.C. and Schepsmeier, U. (2013), “Modeling dependence with C- and D-Vine copulas: the R package CDVine”, Journal of Statistical Software, Vol. 52 No. 3, pp. 1-27, doi: 10.18637/jss.v052.i03.
  11. Chang, C.L. and McAleer, M. (2012), “Aggregation, heterogeneous autoregression and volatility of daily international tourist arrivals and exchange rates”, The Japanese Economic Review, Vol. 63 No. 3, pp. 397-419, doi: 10.1111/j.1468-5876.2011.00563.x.
  12. Chitkasame, T. and Tansuchat, R. (2019), “An analysis of contagion effect on ASEAN stock market using multivariate Markov switching DCC GARCH”, Thai Journal of Mathematics, pp. 135-152, Special Issue, 2019.
  13. Chollets, L., de la Peña, V. and Lu, C.C. (2011), “International diversification: a copula approach”, Journal of Banking and Finance, Vol. 35 No. 2, pp. 403-417, doi: 10.1016/j.jbankfin.2010.08.020.
  14. Chopra, M. and Mehta, C. (2022), “Is the COVID-19 pandemic more contagious for the Asian stock markets? A comparison with the Asian financial, the US subprime and the Eurozone debt crisis”, Journal of Asian Economics, Vol. 79, 101450, available at: https://doi.org/10.1016/j.asieco.2022.101450 (accessed 5 March 2022).
  15. Chowdhury, B., Dungey, M., Kangogo, M., Abu Sayeed, M. and Volkov, V. (2018), “The changing network of financial market linkages: the Asian experience”, ADB Economics Working Paper Series No. 558, Asian Development Bank, September 2018.
  16. Click, R.W. and Plummer, M.G. (2005), “Stock market integration in ASEAN after the Asian financial crisis”, Journal of Asian Economics, Vol. 16 No. 1, pp. 5-28, doi: 10.1016/j.asieco.2004.11.018.
  17. Cortes, G.S., Gao, G.P., Silva, F.B.G. and Song, Z. (2022), “Unconventional monetary policy and disaster risk: evidence from the subprime and COVID–19 crises”, Journal of International Money and Finance, Vol. 122, 102543, available at: https://doi.org/10.1016/j.jimonfin.2021.102543 (accessed 5 March 2022).
  18. Duong, D. and Huynh, T.L.D. (2020), “Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches”, Financial Innovation, Vol. 6 No. 1, pp. 1-26, doi: 10.1186/s40854-019-0168-7.
  19. Genest, C., Ghoudi, K. and Rivest, L.P. (1995), “A semiparametric estimation procedure of dependence parameters in multivariate families of distributions”, Biometrika, Vol. 82 No. 3, pp. 543-552, doi: 10.1093/biomet/82.3.543.
  20. Ghalanos, A. (2020), “Package ‘rugarch’: univariate GARCH models”, available at: https://cran.r-project.org/web/packages/rugarch/rugarch.pdf (accessed 15 March 2021).
  21. Huang, W.Q., Zhuang, X.T., Yao, S. and Uryasev, S. (2016), “A financial network perspective of financial institutions' systemic risk contributions”, Physica A, Vol. 456, pp. 183-196, doi: 10.1016/j.physa.2016.03.034.
  22. Isogai, T. (2017), “Dynamic correlation network analysis of financial asset returns with network clustering”, Applied Network Science, Vol. 2 No. 8, pp. 1-30, doi: 10.1007/s41109-017-0031-6.
  23. Jakpar, S., Vejayon, V., Johari, A. and Myint, K.T. (2013), “An econometric analysis on the co-movement of stock market volatility between China and ASEAN-5”, International Journal of Business and Social Science, Vol. 4 No. 14, pp. 181-197.
  24. Janor, H. and Ali, R. (2007), “Financial integration of the ASEAN-5 markets: financial crisis effects based on bivariate and multivariate cointegration approach”, Investment Management and Financial Innovations, Vol. 4 No. 4, pp. 144-158.
  25. Lean, H.H. and Smyth, R. (2014), “Stock market co-movement in ASEAN and China”, in Arouri, M., Boubaker, S. and Nguyen, D.K. (Eds), Emerging Markets and the Global Economy, Elsevier, Oxford, pp. 603-622.
  26. Lim, L.K. (2007), “Linkages between ASEAN stock markets: a cointegration approach”, in MODSIM 2007 International Congress on Modelling and Simulation, Modelling and simulation society of Australia and New Zealand, pp. 1818-1824.
  27. Liu, H.Y., Manzoor, A., Wang, C.Y., Zhang, L. and Manzoor, Z. (2020), “The COVID-19 outbreak and affected countries stock markets response”, International Journal of Environmental Research and Public Health, Vol. 17 No. 8, p. 2800, available at: https://doi.org/10.3390/ijerph17082800 (accessed 31 August 2020).
  28. Long, H., Zhang, J. and Tang, N. (2017), “Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market”, PLoS ONE, Vol. 12 No. 7, e0180382, available at: https://doi.org/10.1371/journal.pone.0180382 (accessed 23 April 2021).
  29. Mantegna, R.N. (1999), “Hierarchical structure in financial markets”, The European Physical Journal B – Condensed Matter and Complex Systems, Vol. 11 No. 1, pp. 193-197, doi: 10.1007/s100510050929.
  30. Marsila, S., Ruslan, M. and Mokhtar, K. (2021), “Stock market volatility on shipping stock prices: GARCH models approach”, The Journal of Economic Asymmetries, Vol. 24, e00232, available at: https://doi.org/10.1016/j.jeca.2021.e00232 (accessed 5 March 2022).
  31. Millington, T. and Niranjan, M. (2021), “Construction of minimum spanning trees from financial returns using rank correlation”, Physica A: Statistical Mechanics and Its Applications, Vol. 566, 125605, available at: https://doi.org/10.1016/j.physa.2020.125605 (accessed 28 March 2022).
  32. Nelsen, R.B. (2006), An Introduction to Copulas, 2nd ed., Springer Science Business Media, New York.
  33. Onnela, J.P., Chakraborti, A., Kaski, K. and Kertész, J. (2002), “Dynamic asset trees and portfolio analysis”, The European Physical Journal B, Vol. 30 No. 3, pp. 285-288, doi: 10.1140/epjb/e2002-00380-9.
  34. Pastpipatkul, P., Yamaka, W., Sriboonchitta, S. (2016), “Effect of quantitative easing on ASEAN-5 financial markets”, in Huynh, V.N., Kreinovich, V. and Sriboonchitta, S. (Eds), Causal Inference in Econometrics, Studies in Computational Intelligence, Vol. 622, Springer, Cham.
  35. Patton, A.J. (2001), “Modelling time-varying exchange rate dependence using the conditional copula”, UCSD Discussion Paper No. 01-09, University of California, San Diego. available at: http://dx.doi.org/10.2139/ssrn.275591 (accessed 10 December 2020).
  36. Patton, A.J. (2006), “Modelling asymmetric exchange rate dependence”, International Economic Review, Vol. 47 No. 2, pp. 527-556, doi: 10.1111/j.1468-2354.2006.00387.x.
  37. Pongkongkaew, P., Wannapan, S., Chaitip, P. and Chaiboonsri, C. (2020), “Modeling dependence structure of evidence from ASEAN-5 stock market patterns”, International Journal of Economics and Management, Vol. 14 No. 1, pp. 81-94.
  38. Puarattanaarunkorn, O., Kiatmanaroch, T. and Sriboonchitta, S. (2016), “Dependence between volatility of stock price index returns and volatility of exchange rate returns under QE programs: case studies of Thailand and Singapore”, in Huynh, V.N., Kreinovich, V. and Sriboonchitta, S. (Eds), Causal Inference in Econometrics, Studies in Computational Intelligence, Vol. 622, Springer International Publishing, pp. 415-435.
  39. Rebucci, A., Hartley, J.S. and Jiménez, D. (2022), “An event study of COVID-19 central bank quantitative easing in advanced and emerging economies”, in Chudik, A., Hsiao, C. and Timmermann, A. (Eds), Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modelling, Advances in Econometrics Vol. 43A, Emerald Publishing, Bingley, pp. 291-322.
  40. Rillo, A.D. (2018), “Asean financial integration: opportunities, risks, and challenges”, Public Policy Review, Vol. 14 No. 5, pp. 901-924.
  41. Sklar, A. (1959), Fonctions de repartition an dimensions et leurs marges, Vol. 8, Publ. Inst. Statist. Univ, Paris, pp. 229-231.
  42. Sriboonchitta, S., Nguyen, H.T., Wiboonpongse, A. and Liu, J. (2013), “Modeling volatility and dependency of agricultural price and production indices of Thailand: static versus time-varying copulas”, International Journal of Approximate Reasoning, Vol. 54 No. 6, pp. 793-808, doi: 10.1016/j.ijar.2013.01.004.
  43. Sriboonchitta, S., Liu, J., Kreinovich, V. and Nguyen, H.T. (2014), “A Vine copula approach for analyzing Financial risk and co-movement of the Indonesian, Philippine and Thailand stock markets”, in Huynh, V.N., Kreinovich, V. and Sriboonchitta, S. (Eds), Modeling Dependence in Econometrics. Advances in Intelligent Systems and Computing, Vol. 251, Springer, Heidelberg, pp. 245-257.
  44. World Bank (2020), “Global economic prospects, June 2020”, World Bank, Washington. https://openknowledge.worldbank.org/handle/10986/33748 (Accessed 8 September 2020).
  45. Wu, C.C., Chung, H. and Chang, Y.H. (2012), “The economic value of co-movement between oil price and exchange rate using copula based GARCH models”, Energy Economics, Vol. 34 No. 1, pp. 270-282, doi: 10.1016/j.eneco.2011.07.007.
  46. Zhang, D., Hua, M. and Ji, Q. (2020), “Financial markets under the global pandemic of COVID-19”, Finance Research Letters, Vol. 36, 101528, available at: https://doi.org/10.1016/j.frl.2020.10152 (accessed 28 March 2022).

Related articles