World Scientific
  • Search
    Quick Search in Journals
    Access type:
    Quick Search anywhere
    Access type:
    Advanced Search
  • My Cart
  •   
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
No Access

DYNAMIC VOLATILITY SPILLOVERS ACROSS THE EXTENDED GREATER CHINA REGION STOCK MARKETS

    https://doi.org/10.1142/S2194565921500032Cited by:2 (Source: Crossref)
    Previous
    Next

    Abstract

    The study investigates volatility transmissions of major financial market indices found in the Extended Greater China Region (EGCR): the Shanghai Stock Exchange Index (SSEI), the Hang Seng Stock Exchange Index (HSEI), the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), and the Singapore Stock Exchange Index (STI). This paper utilizes three Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models to determine variance persistence in the EGCR. The MGARCH approach applies the Baba–Engle–Kraft–Kroner (BEKK) model and the dynamic conditional correlation (DCC) and constant conditional correlation (CCC) estimations. This research found that the SSEI consistently has the highest volatility among the stock markets in the EGCR, and this can be explained by the poor acknowledgment of minority shareholders’ rights or having a weak so-called “common-law” regime. The paper also found that volatilities in the EGCR are determined strongly by their own lagged values more than the product of lagged cross-products of shocks. This means that financial market movements in the EGCR are still strongly influenced by internal factors more than the external influences. The paper contributes to the literature by understanding the potential changes in volatility relationships in low- and high-volatility regimes of the EGCR, which can be used to improve risk management practices and asset allocation techniques in the region.

    JEL Classifications: G15, G18

    References

    • Allen, DE, R Amram and M McAleer [2013] Volatility spillovers from the Chinese stock market to economic neighbours. Mathematics and Computers in Simulation, 94, 238–257. Crossref, Google Scholar
    • Alotaibi, AR and AV Mishra [2015] Global and regional volatility spillovers to GCC stock markets. Economic Modelling, 45, 38–49. Crossref, Google Scholar
    • Arestis, P, P Demetriades and K Luintel [2001] Financial development and economic growth: The role of stock markets. Journal of Money, Credit and Banking, 33, 16–41. Crossref, Google Scholar
    • Barton, J and G Waymire [2004] Investor protection under unregulated financial reporting. Journal of Accounting and Economics, 38, 65–116. Crossref, Google Scholar
    • Bauwens, L, S Laurent and J Rombouts [2006] Multivariate GARCH models: A survey. Journal of Applied Econometrics, 21, 79–109. Crossref, Google Scholar
    • Bhandari, A, P Yazir and K Hafsal [2016] Co-movements and volatility spillover in Asian forex market: A multivariate GARCH and MRA approach. The Empirical Economics Letters, 15(4), 391–404. Google Scholar
    • Bollerslev, T [1990] Modeling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. The Review of Economics and Statistics, 72, 498–505. Crossref, Google Scholar
    • Bollerslev, T, R Chou and K Kroner [1992] ARCH modeling in finance: A selective review of the theory and empirical evidence, Journal of Econometrics, 52, 5–59. Crossref, Google Scholar
    • Caporin, M and M McAleer [2009] Scalar BEKK and indirect DCC. Journal of Forecasting, 27, 537–549. Crossref, Google Scholar
    • Chang, C-L, M McAleer and R Tansuchat [2011] Crude oil hedging strategies using dynamic multivariate GARCH. Energy Economics, 33(5), 912–923. Crossref, Google Scholar
    • Diaz, JF [2018] Volatility dynamics in the ASEAN-China free trade agreement. Journal of Emerging Market Finance, 17(3), 287–306. Crossref, Google Scholar
    • Du, X, C Yu and D Hayes [2011] Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33(3), 497–503. Crossref, Google Scholar
    • Engle, R [1982] Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987–1007. Crossref, Google Scholar
    • Engle, R [2002] Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business and Economic Statistics, 20(3), 339–350. Crossref, Google Scholar
    • Engle, R and F Kroner [1995] Multivariate simultaneous generalized ARCH. Econometric Theory, 11(1), 122–150. Crossref, Google Scholar
    • Engle, R and K Sheppard (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. Mimeo, University of California at San Diego (UCSD). Google Scholar
    • Fleming, J, C Kirby and B Ostdiek [1998] Information and volatility linkages in the stock, bond and money markets. Journal of Financial Economics, 49(1), 111–137. Crossref, Google Scholar
    • Harding, H [1993] The concept of Greater China: Themes, variations, and reservations. The China Quarterly, 136, 660–686. Crossref, Google Scholar
    • Ho, K, A Tsui and Z Zhang [2009] Volatility dynamics of the US business cycle: A multivariate asymmetric GARCH approach. Mathematics and Computers in Simulation, 79(9), 2856–2868. Crossref, Google Scholar
    • Ho, KY and ZY Zhang [2010] Financial market integration in the EGCR: A multivariate asymmetric approach. Proceedings of the International Conference on Globalization, Monetary Integration and Exchange Rate Regimes in East Asia, Australia. Google Scholar
    • Hosking, J [1980] The multivariate portmanteau statistic. Journal of American Statistical Association, 75(371), 602–608. Crossref, Google Scholar
    • Johansson, AC and C Ljungwall [2009] Spillover effects among the greater China stock markets. World Development, 37(4), 839–851. Crossref, Google Scholar
    • La Porta, R, F Lopez-de-Silanes and A Shleifer (2007) The Economic Consequences of Legal Origins. National Bureau of Economic Research Working Paper 13608. Google Scholar
    • La Porta, R, F Lopez-de-Silanes, A Shleifer and R Vishny [1998] Law and finance. Journal of Political Economy, 106(6), 1113–1155. Crossref, Google Scholar
    • Majdoub, J and W Mansour [2014] Islamic equity market integration and volatility spillover between emerging and US stock markets. The North American Journal of Economics and Finance, 29, 452–470. Crossref, Google Scholar
    • McAleer, M [2005] Automated inference and learning in modeling financial volatility. Econometric Theory, 21(1), 232–261. Crossref, Google Scholar
    • McAleer, M, F Chan and D Marinova [2007] An econometric analysis of asymmetric volatility: Theory and application to patents, Journal of Econometrics, 139, 259–284. Crossref, Google Scholar
    • Poterba, JM [2000] Stock market wealth and consumption. Journal of Economic Perspectives, 14(2), 99–118. Crossref, Google Scholar
    • Rajagopalan, U, SD Sundarasen and N Rajangam [2014] Investors’ protection, corruption and legal origin on the equity market volatility: A global perspective. Asian Social Science, 10(11), 269–276. Crossref, Google Scholar
    • Rehman, MU [2016] Co-movements and volatility spillover in Asian forex market: A multivariate GARCH and MRA approach. The Lahore Journal of Economics, 21(2), 121–151. Google Scholar
    • Silvennoinen, A and T Terasvirta (2008). Multivariate GARCH models. SSE/EFI Working Paper Series in Economics and Finance No. 669, Stockholm School of Economics. Google Scholar
    • So, MK and ASL Tse [2009] Dynamic modeling of tail risk: Applications to China, Hong Kong and other Asian markets. Asia-Pacific Financial Markets, 16, 183–210. Crossref, Google Scholar
    • Tripathy, N [2017] Financial contagion in EFA markets in crisis periods: A multivariate GARCH dynamic conditional correlation framework. International Journal of Business and Emerging Markets, 9(2), 104–123. Crossref, Google Scholar
    • Worthington, A, A Kay-Spratley and H Higgs [2005] Transmission of prices and price volatility in Australian electricity spot markets: A multivariate GARCH analysis. Energy Economics, 27(2), 337–350. Crossref, Google Scholar
    • Yu, IW, KP Fung and CS Tam [2010] Assessing financial market integration in asian equity markets. Journal of Banking and Finance, 34(12), 2874–2885. Crossref, Google Scholar
    • Zhu, J [2009] Testing for expected return and market price of risk in Chinese A and B share markets: A geometric Brownian motion and MGARCH model approach. Mathematics and Computers in Simulation, 79(8), 2633–2653. Crossref, Google Scholar
    • Zuliu, H (1995). Stock market volatility and corporate investment. IMF Working Paper, 95–102. Google Scholar