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The dynamics of return co-movements and volatility spillover effects in Greater China public property markets and international linkages

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  • Kim Hiang Liow

Abstract

Purpose - – The purpose of this paper is to examine weekly dynamic conditional correlations (DCC) and vector autoregressive (VAR)-based volatility spillover effects within the three Greater China (GC) public property markets, as well as across the GC property markets, three Asian emerging markets and two developed markets of the USA and Japan over the period from January 1999 through December 2013. Design/methodology/approach - – First, the author employ the DCC methodology proposed by Engle (2002) to examine the time-varying nature in return co-movements among the public property markets. Second, the author appeal to the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) to investigate the volatility spillover effects across the real estate markets. Finally, the spillover framework is able to combine with recent developments in time series econometrics to provide a comprehensive analysis of the dynamic volatility co-movements regionally and globally. The author also examine whether there are volatility spillover regimes, as well as explore the relationship between the volatility spillover cycles and the correlation spillover cycles. Findings - – Results indicate moderate return co-movements and volatility spillover effects within and across the GC region. Cross-market volatility spillovers are bidirectional with the highest spillovers occur during the global financial crisis (GFC) period. Comparatively, the Chinese public property market's volatility is more exogenous and less influenced by other markets. The volatility spillover effects are subject to regime switching with two structural breaks detected for the five sub-groups of markets examined. There is evidence of significant dependence between the volatility spillover cycles across stock and public real estate, due to the presence of unobserved common shocks. Research limitations/implications - – Because international investors incorporate into their portfolio allocation not only the long-term price relationship but also the short-term market volatility interaction and return correlation structure, the results of this study can shed more light on the extent to which investors can benefit from regional and international diversification in the long run and short-term within and across the GC securitized property sector, with Asian emerging market and global developed markets of Japan and USA. Although it is beyond the scope of this paper, it would be interesting to examine how the two co-movement measures (volatility spillovers and correlation spillovers) can be combined in optimal covariance forecasting in global investing that includes stock and public real estate markets. Originality/value - – This is one of very few papers that comprehensively analyze the dynamic return correlations and conditional volatility spillover effects among the three GC public property markets, as well as with their selected emerging and developed partners over the last decade and during the GFC period, which is the main contribution of the study. The specific contribution is to characterize and measure cross-public real estate market volatility transmission in asset pricing through estimates of several conditional “volatility spillover” indices. In this case, a volatility spillover index is defined as share of total return variability in one public real estate market attributable to volatility surprises in another public real estate market.

Suggested Citation

  • Kim Hiang Liow, 2014. "The dynamics of return co-movements and volatility spillover effects in Greater China public property markets and international linkages," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 32(6), pages 610-641, August.
  • Handle: RePEc:eme:jpifpp:v:32:y:2014:i:6:p:610-641
    DOI: 10.1108/JPIF-06-2014-0039
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    2. Amanjot Singh & Parneet Kaur, 2017. "A Short Note on Information Transmissions Across US-BRIC Equity Markets: Evidence from Volatility Spillover Index," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 15(1), pages 197-208, March.
    3. Lee, Hahn Shik & Lee, Woo Suk, 2019. "Cross-regional connectedness in the Korean housing market," Journal of Housing Economics, Elsevier, vol. 46(C).
    4. Mário Nuno Mata & Muhammad Najib Razali & Sónia R. Bentes & Isabel Vieira, 2021. "Volatility Spillover Effect of Pan-Asia’s Property Portfolio Markets," Mathematics, MDPI, vol. 9(12), pages 1-20, June.
    5. Chien-Fu Chen & Shu-hen Chiang, 2020. "Time-varying spillovers among first-tier housing markets in China," Urban Studies, Urban Studies Journal Limited, vol. 57(4), pages 844-864, March.
    6. Choi, Sun-Yong, 2022. "Credit risk interdependence in global financial markets: Evidence from three regions using multiple and partial wavelet approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    7. Choi, Sun-Yong, 2022. "Volatility spillovers among Northeast Asia and the US: Evidence from the global financial crisis and the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 179-193.
    8. Liow, Kim Hiang, 2015. "Volatility spillover dynamics and relationship across G7 financial markets," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 328-365.
    9. Li, Yanshuang & Zhuang, Xintian & Wang, Jian, 2021. "Analysis of the cross-region risk contagion effect in stock market based on volatility spillover networks: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).

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