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Housing Market Spillovers in China via Trading Volume: A Comparison of Two Spillover Indexes

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  • Jian Yang
  • Ziliang Yu

Abstract

Proposing a new spillover/connectedness index based on data‐determined structural vector autoregression (SVAR) with non‐Gaussian residuals, this paper investigates nationwide housing market spillovers via transaction volumes among China's largest cities. Significant daily information transmission is documented across geographically dispersed cities, with a pattern that the first‐tier cities generally play a more important role than others in the spillovers. Economic factors (e.g., income) and human capital (e.g., education) are significant determinants of the spillovers. The pattern based on the new spillover index is also substantially better explained by fundamentals than those based on the widely used Diebold‐Yilmaz spillover index.

Suggested Citation

  • Jian Yang & Ziliang Yu, 2025. "Housing Market Spillovers in China via Trading Volume: A Comparison of Two Spillover Indexes," The Financial Review, Eastern Finance Association, vol. 60(4), pages 1337-1357, November.
    • Handle: RePEc:bla:finrev:v:60:y:2025:i:4:p:1337-1357
    DOI: 10.1111/fire.70004
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    1. Jian Yang & Yinggang Zhou, 2013. "Credit Risk Spillovers Among Financial Institutions Around the Global Credit Crisis: Firm-Level Evidence," Management Science, INFORMS, vol. 59(10), pages 2343-2359, October.
    2. Badarinza, Cristian & Ramadorai, Tarun, 2018. "Home away from home? Foreign demand and London house prices," Journal of Financial Economics, Elsevier, vol. 130(3), pages 532-555.
    3. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    4. Fernando Ferreira & Joseph Gyourko, 2012. "Heterogeneity in Neighborhood-Level Price Growth in the United States, 1993-2009," American Economic Review, American Economic Association, vol. 102(3), pages 134-140, May.
    5. Hanming Fang & Quanlin Gu & Wei Xiong & Li-An Zhou, 2016. "Demystifying the Chinese Housing Boom," NBER Macroeconomics Annual, University of Chicago Press, vol. 30(1), pages 105-166.
    6. Halling, Michael & Moulton, Pamela C. & Panayides, Marios, 2013. "Volume Dynamics and Multimarket Trading," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(2), pages 489-518, April.
    7. Antoine Giannetti, 2021. "Home Sales Pair Counts: The Organic Metric for Trading Volume in Housing Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(2), pages 610-634, June.
    8. Berardi, Andrea & Plazzi, Alberto, 2022. "Dissecting the yield curve: The international evidence," Journal of Banking & Finance, Elsevier, vol. 134(C).
    9. Edward E. Leamer, 2015. "Housing Really Is the Business Cycle: What Survives the Lessons of 2008–09?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 43-50, March.
    10. Jingchi Liao & Cameron Peng & Ning Zhu, 2022. "Extrapolative Bubbles and Trading Volume," The Review of Financial Studies, Society for Financial Studies, vol. 35(4), pages 1682-1722.
    11. Du, Zaichao & Zhang, Lin, 2015. "Home-purchase restriction, property tax and housing price in China: A counterfactual analysis," Journal of Econometrics, Elsevier, vol. 188(2), pages 558-568.
    12. Gouriéroux, Christian & Monfort, Alain & Renne, Jean-Paul, 2017. "Statistical inference for independent component analysis: Application to structural VAR models," Journal of Econometrics, Elsevier, vol. 196(1), pages 111-126.
    13. Junhong Chu & Yige Duan & Xianling Yang & Li Wang, 2021. "The Last Mile Matters: Impact of Dockless Bike Sharing on Subway Housing Price Premium," Management Science, INFORMS, vol. 67(1), pages 297-316, January.
    14. Wang, Jiang, 1994. "A Model of Competitive Stock Trading Volume," Journal of Political Economy, University of Chicago Press, vol. 102(1), pages 127-168, February.
    15. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    16. Eichengreen, Barry & Mody, Ashoka & Nedeljkovic, Milan & Sarno, Lucio, 2012. "How the Subprime Crisis went global: Evidence from bank credit default swap spreads," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1299-1318.
    17. DeFusco, Anthony A. & Nathanson, Charles G. & Zwick, Eric, 2022. "Speculative dynamics of prices and volume," Journal of Financial Economics, Elsevier, vol. 146(1), pages 205-229.
    18. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    19. William Goetzmann & Liang Peng, 2006. "Estimating House Price Indexes in the Presence of Seller Reservation Prices," The Review of Economics and Statistics, MIT Press, vol. 88(1), pages 100-112, February.
    20. Wheaton, William C, 1990. "Vacancy, Search, and Prices in a Housing Market Matching Model," Journal of Political Economy, University of Chicago Press, vol. 98(6), pages 1270-1292, December.
    21. Yinglu Deng & Li Liao & Jiaheng Yu & Yu Zhang, 2022. "Capital Spillover, House Prices, and Consumer Spending: Quasi-Experimental Evidence from House Purchase Restrictions," The Review of Financial Studies, Society for Financial Studies, vol. 35(6), pages 3060-3099.
    22. Lo, Andrew W & Wang, Jiang, 2000. "Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory," The Review of Financial Studies, Society for Financial Studies, vol. 13(2), pages 257-300.
    23. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2022. "Assessing the impact of policy and regulation interventions in European sovereign credit risk networks: What worked best?," Journal of International Economics, Elsevier, vol. 139(C).
    24. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    25. Greg Kaplan & Kurt Mitman & Giovanni L. Violante, 2020. "The Housing Boom and Bust: Model Meets Evidence," Journal of Political Economy, University of Chicago Press, vol. 128(9), pages 3285-3345.
    26. Kaiji Chen & Yi Wen, 2017. "The Great Housing Boom of China," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(2), pages 73-114, April.
    27. repec:nsb:wpaper:21 is not listed on IDEAS
    28. Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2016. "Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1005-1025, September.
    29. Timo Bettendorf & Reinhold Heinlein, 2023. "Connectedness between G10 currencies: Searching for the causal structure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3938-3959, October.
    30. Hoover, Kevin D., 2005. "Automatic Inference Of The Contemporaneous Causal Order Of A System Of Equations," Econometric Theory, Cambridge University Press, vol. 21(1), pages 69-77, February.
    31. He, Hua & Wang, Jiang, 1995. "Differential Information and Dynamic Behavior of Stock Trading Volume," The Review of Financial Studies, Society for Financial Studies, vol. 8(4), pages 919-972.
    32. Liao, Jingchi & Peng, Cameron & Zhu, Ning, 2022. "Extrapolative bubbles and trading volume," LSE Research Online Documents on Economics 110514, London School of Economics and Political Science, LSE Library.
    33. Chang, Yuk-Ying & Dasgupta, Sudipto, 2022. "Capital Inflows and Property Prices: Ethnicity, Education, and Spillovers," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 57(8), pages 3145-3177, December.
    34. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    35. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    36. Lee, Bong-Soo & Rui, Oliver M., 2002. "The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence," Journal of Banking & Finance, Elsevier, vol. 26(1), pages 51-78, January.
    37. Michael Bailey & Ruiqing Cao & Theresa Kuchler & Johannes Stroebel, 2018. "The Economic Effects of Social Networks: Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2224-2276.
    38. John Cotter & Stuart Gabriel & Richard Roll, 2015. "Can Housing Risk Be Diversified? A Cautionary Tale from the Housing Boom and Bust," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 913-936.
    39. Roll, Richard & Schwartz, Eduardo & Subrahmanyam, Avanidhar, 2014. "Trading activity in the equity market and its contingent claims: An empirical investigation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 13-35.
    40. Jian Yang & Meng Tong & Ziliang Yu, 2023. "Can volume be more informative than prices? Evidence from Chinese housing markets," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 633-672, August.
    41. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    42. Yang, Jian & Tong, Meng & Yu, Ziliang, 2021. "Housing market spillovers through the lens of transaction volume: A new spillover index approach," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 351-378.
    43. Pan, Carrie H. & Pirinsky, Christo A., 2015. "Social Influence in the Housing Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(4), pages 757-779, August.
    44. Painter, Gary & Yang, Xi & Zhong, Ninghua, 2022. "Housing Wealth as Precautionary Saving: Evidence from Urban China," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 57(2), pages 761-789, March.
    45. Chen, Shiu-Sheng, 2012. "Revisiting the empirical linkages between stock returns and trading volume," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1781-1788.
    46. Hurn, Stan & Shi, Shuping & Wang, Ben, 2022. "Housing networks and driving forces," Journal of Banking & Finance, Elsevier, vol. 134(C).
    47. Alessio Moneta & Doris Entner & Patrik O. Hoyer & Alex Coad, 2013. "Causal Inference by Independent Component Analysis: Theory and Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 705-730, October.
    48. Roberto A. De Santis & Srečko Zimic, 2018. "Spillovers among sovereign debt markets: Identification through absolute magnitude restrictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 727-747, August.
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