Equity market integration of China and Southeast Asian countries: further evidence from MGARCH-ADCC and wavelet coherence analysis

Ngo Thai Hung; Ngo Thai Hung

doi:10.3934/QFE.2019.2.201

Quantitative Finance and Economics

2019, Volume 3, Issue 2: 201-220. doi: 10.3934/QFE.2019.2.201

Previous Article Next Article

Research article

Equity market integration of China and Southeast Asian countries: further evidence from MGARCH-ADCC and wavelet coherence analysis

Ngo Thai Hung ^,

University of Finance-Marketing, Ho Chi Minh City, Vietnam

Received: 29 December 2019 Accepted: 26 February 2019 Published: 30 April 2019
JEL Codes: G15, F36, C40

This paper investigates the short-term and long-term dynamics between China and four Southeast Asian countries (Vietnam, Thailand, Singapore and Malaysia) during period 2008–2018. Our empirical research is based on the Generalized Autoregression Conditional Heteroscedasticity-Asymmetric Dynamic Conditional Covariance (MGARCH-ADCC) model and the wavelet coherence technique which allow us to estimate the time-varying correlation and the co-movement in both time-frequency spaces of stock markets of China and its neighboring countries. The results of the study reveal that stock markets of China and its trading partners are relatively integrated after the global financial crisis of 2008, frequency changes in the pattern of the co-movements and a positive linkage throughout the sample period. Specifically, the conditional correlation of stock returns between China and Singapore is more significantly influenced by negative innovations than by positive shocks to return. Furthermore, the study provides evidence of significant coherence between both the variables for almost the entire studied period in long scale. Therefore, these findings are positive signs for the Chinese and international investors to diversify their portfolio among the stock markets of China and its trading partners.
- stock market integration,
- China,
- wavelet coherence,
- GARCH-ADCC,
- Southeast Asian
Citation: Ngo Thai Hung. Equity market integration of China and Southeast Asian countries: further evidence from MGARCH-ADCC and wavelet coherence analysis[J]. Quantitative Finance and Economics, 2019, 3(2): 201-220. doi: 10.3934/QFE.2019.2.201

Related Papers:

Abstract

This paper investigates the short-term and long-term dynamics between China and four Southeast Asian countries (Vietnam, Thailand, Singapore and Malaysia) during period 2008–2018. Our empirical research is based on the Generalized Autoregression Conditional Heteroscedasticity-Asymmetric Dynamic Conditional Covariance (MGARCH-ADCC) model and the wavelet coherence technique which allow us to estimate the time-varying correlation and the co-movement in both time-frequency spaces of stock markets of China and its neighboring countries. The results of the study reveal that stock markets of China and its trading partners are relatively integrated after the global financial crisis of 2008, frequency changes in the pattern of the co-movements and a positive linkage throughout the sample period. Specifically, the conditional correlation of stock returns between China and Singapore is more significantly influenced by negative innovations than by positive shocks to return. Furthermore, the study provides evidence of significant coherence between both the variables for almost the entire studied period in long scale. Therefore, these findings are positive signs for the Chinese and international investors to diversify their portfolio among the stock markets of China and its trading partners.

References

[1]	Aloui C, Hkiri B (2014) Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis. Econ Model 36: 421–431. doi: 10.1016/j.econmod.2013.09.043
[2]	Aggarwal S, Raja A (2019) Stock market interlinkages among the BRIC economies. Int J Ethics Syst 35: 59–74. doi: 10.1108/IJOES-04-2018-0064
[3]	Bissoondoyal-Bheenick E, Brooks R, et al. (2018) Volatility spillover between the US, Chinese and Australian stock markets. Aus J Manage 43: 263–285. doi: 10.1177/0312896217717305
[4]	Bai S, Cui W, Zhang L (2018) The Granger causality analysis of stocks based on clustering. Cluster Comput, 1–6.
[5]	Cai XJ, Tian S, Yuan N, et al. (2017) Interdependence between oil and East Asian stock markets: Evidence from wavelet coherence analysis. J Int Financ Mark Inst Money 48: 206–223. doi: 10.1016/j.intfin.2017.02.001
[6]	Cappiello L, Engle RF, Sheppard K (2006) Asymmetric dynamics in the correlations of global equity and bond returns. J Financ Econ 4: 537–572.
[7]	Chien M, Lee C, Hu T, et al. (2015) Dynamic Asian stock market convergence: Evidence from dynamic cointegration analysis among China and ASEAN-5. Econ Model 51: 84–98.
[8]	Caporale G, Ali F, Spagnolo N (2015) Oil price uncertainty and sectoral stock returns in China: A time-varying approach. China Econ Rev 34: 311–321.
[9]	Chiang T, Chen X (2016) Empirical analysis of dynamic linkages between China and international stock markets. J Math Financ 6: 189.
[10]	Caporin M, McAleer M (2013) Ten things you should know about the dynamic conditional correlation representation. Econometrics 1: 115–126. doi: 10.3390/econometrics1010115
[11]	Drometer-Oesingmann K (2015) Household Debt in Central and Eastern Europe CEE. In CESifo Forum, München: ifo Institut–Leibniz-Institut für Wirtschaftsforschung an der Universität München. 16: 82–84.
[12]	Engle R (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20: 339–350 doi: 10.1198/073500102288618487
[13]	Fang L, Bessler D (2018) Is it China that leads the Asian stock market contagion in 2015? Appl Econ Lett 25: 752–757.
[14]	Grinsted A, Moore J, Jevrejeva S(2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys 11: 561–566.
[15]	Hung N (2019) Return and volatility spillover across equity markets between China and Southeast Asian countries. J Econ Financ Administrative Sci.
[16]	Hung N (2018) Volatility Behaviour of the Foreign Exchange Rate and Transmission Among Central and Eastern European Countries: Evidence from the EGARCH Model. Global Bus Rev.
[17]	Huyghebaert N, Wang L (2010) The co-movement of stock markets in East Asia: Did the 1997–1998 Asian financial crisis really strengthen stock market integration? China Econ Rev 21: 98–112.
[18]	Jain A, Biswal P (2016) Dynamic linkages among oil price, gold price, exchange rate, and stock market in India. Resour Policy 49: 179–185.
[19]	Joyo A, Lefen L (2019) Stock Market Integration of Pakistan with Its Trading Partners: A Multivariate DCC-GARCH Model Approach. Sustainability 11: 303. doi: 10.3390/su11020303
[20]	Kumar D (2014) Return and volatility transmission between gold and stock sectors: Application of portfolio management and hedging effectiveness. IIMB Manage Rev 26: 5–16.
[21]	Kao W, Kao T, Changchien C, et al. (2018) Contagion in international stock markets after the subprime mortgage crisis. Chin Econ 51: 130–153. doi: 10.1080/10971475.2018.1447822
[22]	Khan F, Bangash R, Khan M (2018) Dynamic Linkage among Pakistan, Emerging and Developed Equity Market. FWU J Social Sci 112.
[23]	Li Y, Giles D (2015) Modelling volatility spillover effects between developed stock markets and Asian emerging stock markets. Int J Financ Econ 20: 155–177. doi: 10.1002/ijfe.1506
[24]	Li B, Zeng Z (2018) Time-varying dependence structures of equity markets of China, ASEAN and the USA. Appl Econ Lett 25: 87–91. doi: 10.1080/13504851.2017.1296545
[25]	Lee B (2019) Asian financial market integration and the role of Chinese financial market. Int Rev Econ Financ 59: 490–499. doi: 10.1016/j.iref.2018.10.012
[26]	Lau C, Sheng X (2018) Inter-and intra-regional analysis on spillover effects across international stock markets. Res Int Bus Financ 46: 420–429. doi: 10.1016/j.ribaf.2018.04.013
[27]	Majdoub J, Sassi S (2017) Volatility spillover and hedging effectiveness among China and emerging Asian Islamic equity indexes. Emerg Mark Rev 31: 16–31. doi: 10.1016/j.ememar.2016.12.003
[28]	Nagayev R, Disli M, Inghelbrecht K, et al. (2016) On the dynamic links between commodities and Islamic equity. Energy Econ 58: 125–140. doi: 10.1016/j.eneco.2016.06.011
[29]	Najeeb S, Bacha O, Masih M (2015) Does heterogeneity in investment horizons affect portfolio diversification? Some insights using M-GARCH-DCC and wavelet correlation analysis. Emerg Mark Financ Trade 51: 188–208.
[30]	Prasad N, Grant A, Kim S (2018) Time varying volatility indices and their determinants: Evidence from developed and emerging stock markets. Int Rev Financ Anal 60: 115–126. doi: 10.1016/j.irfa.2018.09.006
[31]	Paramati S, Zakari A, Jalle M, et al. (2018) The dynamic impact of bilateral trade linkages on stock market correlations of Australia and China. Appl Econ Lett 25: 141–145. doi: 10.1080/13504851.2017.1305067
[32]	Raza S, Shah N, Sharif A (2019) Time frequency relationship between energy consumption, economic growth and environmental degradation in the United States: Evidence from transportation sector. Energy.
[33]	Raza S, Sharif A, Wong WK, et al. (2017) Tourism development and environmental degradation in the United States: Evidence from wavelet-based analysis. Curr Issues Tour 20: 1768–1790. doi: 10.1080/13683500.2016.1192587
[34]	Ramsey J, Zhang Z (1996) The application of wave form dictionaries to stock market index data. In Predictability of complex dynamical systems, Springer, 189–205.
[35]	Rizvi S, Arshad S (2014) An empirical study of Islamic equity as a better alternative during crisis using multivariate GARCH DCC. Islamic Econ Stud 130: 1–27.
[36]	Roni B, Abbas G, Wang S (2018) Return and Volatility Spillovers Effects: Study of Asian Emerging Stock Markets. J Syst Sci Inf 6: 97–119.
[37]	Kirkulak Uludag B, Khurshid M (2019) Volatility spillover from the Chinese stock market to E7 and G7 stock markets. J Econ Stud 46: 90–105. doi: 10.1108/JES-01-2017-0014
[38]	Sehgal S, Pandey P, Deisting F (2018) Stock market integration dynamics and its determinants in the East Asian Economic Community Region. J Quant Econ 16: 389–425. doi: 10.1007/s40953-017-0090-7
[39]	Sznajderska A (2019) The role of China in the world economy: evidence from a global VAR model. Appl Econ 51: 1574–1587. doi: 10.1080/00036846.2018.1527464
[40]	Torrence C, Webster P (1999) Interdecadal changes in the ENSO–monsoon system. J clim 12: 2679–2690. doi: 10.1175/1520-0442(1999)012<2679:ICITEM>2.0.CO;2
[41]	Tamakoshi G, Hamori S (2013) An asymmetric dynamic conditional correlation analysis of linkages of European financial institutions during the Greek sovereign debt crisis. Eur J Financ 19: 939–950. doi: 10.1080/1351847X.2012.712921
[42]	Toyoshima Y, Tamakoshi G, Hamori S (2012) Asymmetric dynamics in correlations of treasury and swap markets: Evidence from the US market. J Int Financ Mark Inst Money 22: 381–394. doi: 10.1016/j.intfin.2011.12.002
[43]	Yarovaya L, Brzeszczyński J, Lau C (2016) Volatility spillovers across stock index futures in Asian markets: Evidence from range volatility estimators. Financ Res Lett 17: 158–166. doi: 10.1016/j.frl.2016.03.005
[44]	Yilmaz K (2010) Return and volatility spillovers among the East Asian equity markets. J Asian Econ 21: 304–313. doi: 10.1016/j.asieco.2009.09.001
[45]	Yang L, Cai XJ, Zhang H, et al. (2016) Interdependence of foreign exchange markets: A wavelet coherence analysis. Econ Model 55: 6–14. doi: 10.1016/j.econmod.2016.01.022
[46]	Yang L, Cai XJ, Hamori S (2017). Does the crude oil price influence the exchange rates of oil-importing and oil-exporting countries differently? A wavelet coherence analysis. Int Rev Econ Financ 49: 536–547.
[47]	Zhou X, Zhang W, Zhang J (2012) Volatility spillovers between the Chinese and world equity markets. Pac-Basin Financ J 20: 247–270. doi: 10.1016/j.pacfin.2011.08.002

Reader Comments

your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)