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Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

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  • Gu, Huaying
  • Liu, Zhixue
  • Weng, Yingliang

Abstract

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

Suggested Citation

  • Gu, Huaying & Liu, Zhixue & Weng, Yingliang, 2017. "Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 460-472.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:460-472
    DOI: 10.1016/j.physa.2016.12.056
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    as
    1. Yang, Miao & Jiang, Zhi-Qiang, 2016. "The dynamic correlation between policy uncertainty and stock market returns in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 92-100.
    2. Edoardo Otranto & Massimo Mucciardi & Pietro Bertuccelli, 2016. "Spatial effects in dynamic conditional correlations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 604-626, March.
    3. Christoffersen, Peter & Errunza, Vihang & Jacobs, Kris & Jin, Xisong, 2014. "Correlation dynamics and international diversification benefits," International Journal of Forecasting, Elsevier, vol. 30(3), pages 807-824.
    4. Bing Zhu & Roland Füss & Nico B. Rottke, 2013. "Spatial Linkages in Returns and Volatilities among U.S. Regional Housing Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 41(1), pages 29-64, March.
    5. Luc Bauwens & Edoardo Otranto, 2016. "Modeling the Dependence of Conditional Correlations on Market Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 254-268, April.
    6. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
    7. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    8. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
    9. Gian Piero Aielli, 2013. "Dynamic Conditional Correlation: On Properties and Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 282-299, July.
    10. Stošić, Darko & Stošić, Dusan & Stošić, Tatijana & Stanley, H. Eugene, 2015. "Multifractal analysis of managed and independent float exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 13-18.
    11. Dimitris Kenourgios & Dimitrios Dimitriou, 2014. "Contagion Effects of the Global Financial Crisis in US and European Real Economy Sectors," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 61(3), pages 275-288, June.
    12. Case, Anne C. & Rosen, Harvey S. & Hines, James Jr., 1993. "Budget spillovers and fiscal policy interdependence : Evidence from the states," Journal of Public Economics, Elsevier, vol. 52(3), pages 285-307, October.
    13. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    14. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
    15. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    16. Wang, Gang-Jin & Xie, Chi, 2015. "Correlation structure and dynamics of international real estate securities markets: A network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 176-193.
    17. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    18. Yudong Wang & Li Liu, 2016. "Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging," Empirical Economics, Springer, vol. 50(4), pages 1481-1509, June.
    19. Maria Kasch & Massimiliano Caporin, 2013. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," Journal of Financial Econometrics, Oxford University Press, vol. 11(4), pages 706-742, September.
    20. Bauwens, Luc & Grigoryeva, Lyudmila & Ortega, Juan-Pablo, 2016. "Estimation and empirical performance of non-scalar dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 17-36.
    21. Pedro Nielsen Rotta & Pedro L. Valls Pereira, 2016. "Analysis of contagion from the dynamic conditional correlation model with Markov Regime switching," Applied Economics, Taylor & Francis Journals, vol. 48(25), pages 2367-2382, May.
    22. Chang, Guang-Di & Chen, Chia-Shih, 2014. "Evidence of contagion in global REITs investment," International Review of Economics & Finance, Elsevier, vol. 31(C), pages 148-158.
    23. Mollah, Sabur & Quoreshi, A.M.M. Shahiduzzaman & Zafirov, Goran, 2016. "Equity market contagion during global financial and Eurozone crises: Evidence from a dynamic correlation analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 151-167.
    24. Tse, Chin-Bun & Rodgers, Timothy & Niklewski, Jacek, 2014. "The 2007 financial crisis and the UK residential housing market: Did the relationship between interest rates and house prices change?," Economic Modelling, Elsevier, vol. 37(C), pages 518-530.
    25. Dror Y. Kenett & Xuqing Huang & Irena Vodenska & Shlomo Havlin & H. Eugene Stanley, 2015. "Partial correlation analysis: applications for financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 569-578, April.
    26. Tamakoshi, Go & Hamori, Shigeyuki, 2014. "Co-movements among major European exchange rates: A multivariate time-varying asymmetric approach," International Review of Economics & Finance, Elsevier, vol. 31(C), pages 105-113.
    27. Reboredo, Juan C. & Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu, 2015. "An analysis of dependence between Central and Eastern European stock markets," Economic Systems, Elsevier, vol. 39(3), pages 474-490.
    28. Hemche, Omar & Jawadi, Fredj & Maliki, Samir B. & Cheffou, Abdoulkarim Idi, 2016. "On the study of contagion in the context of the subprime crisis: A dynamic conditional correlation–multivariate GARCH approach," Economic Modelling, Elsevier, vol. 52(PA), pages 292-299.
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    Cited by:

    1. Karatetskaya Efrosiniya & Lakshina Valeriya, 2018. "Volatility Spillovers With Spatial Effects On The Oil And Gas Market," HSE Working papers WP BRP 72/FE/2018, National Research University Higher School of Economics.
    2. Sidong Zhao & Kaixu Zhao & Ping Zhang, 2021. "Spatial Inequality in China’s Housing Market and the Driving Mechanism," Land, MDPI, vol. 10(8), pages 1-33, August.
    3. Zouheir Mighri & Raouf Jaziri, 2023. "Long-Memory, Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk Estimation: Empirical Evidence from the Global Real Estate Markets," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 41-97, March.
    4. Liang, Cong & Hui, Eddie C.M. & Yip, Tsz Leung & Huang, Yaoxuan, 2020. "Private land use for public housing projects: The Influence of a Government Announcement on Housing Markets in Hong Kong," Land Use Policy, Elsevier, vol. 99(C).
    5. Liang, Cong & Hui, Eddie C.M. & Yip, Tsz Leung, 2018. "Time on market (TOM): The impact of new residential stamp duty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1117-1130.

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