Summary:
At present, China's economic development faces the threefold pressure of demand contraction, supply shock, and expected recession. The liquidity risk, delivery risk, debt default risk, and credit risk caused by ruptures in the capital chain of real estate companies may not only hinder economic growth but also induce systemic financial risks. Therefore, to control financial risks and ensure stable economic growth, it is important to accurately measure real estate companies' financial risk and systemically identify companies in need of supervision or rescue. This is also in line with the goal of “promoting a virtuous circle and healthy development of the real estate industry” set out at the 2021 Central Economic Work Conference. This paper uses data on listed real estate companies in China's A-share market. First, based on the SIM single-index quantile regression method, a new systemic risk indicator SIM-CoVaR is proposed, and the systemic risk of listed real estate companies is measured. Next, by combining it with the forward-looking TENET model, we construct dynamically evolving real estate market tail risk networks and reveal the time-varying spillover effect of systemic risk in the real estate market through the dynamic network topology. Second, we calculate the systemic risk contribution index and exposure index, and we capture systemically important companies with strong tail risk correlation through the index ranking. Furthermore, through the block model method, we explore the clustering of systemic risk spillover effects and the contagion path between blocks. Finally, we investigate the influence of the overall network structure on systemic risk spillovers in the real estate market. The empirical results show the following. (1) There is an extensive systemic risk co-movement and spillover effects across real estate companies, and the tail risk network presents heterogeneity. A diversified business structure strengthens the diversified relationship between real estate companies, broadens the channels for dispersing risks, and further reduces the risk spillover level of the overall real estate market. (2) The risk spillover intensity between real estate companies during the 2008 financial crisis was greater than that during the 2015 domestic stock market crash. This difference is affected by the external economy, related policy, and internal linkage, and the real estate industry has become an amplifier of systemic risk spillovers during economic recessions. (3) The systemic risk spillover effect has significant clustering characteristics in the tail risk network. The tail risk network can be divided into four blocks with different functions. In the process of systemic risk transmission, the roles and functions of each block show clear time-varying characteristics. (4) From the perspective of the impact mechanism of spillover effects, the reduction of network aggregation, network efficiency, and network matching can significantly reduce systemic risk spillover. In addition, during periods of economic prosperity and steady development, the real estate sector tends to hide potential risks, and the spillover effects of systemic risks are weakened. The study makes the following contributions. First, TENET incorporates more companies into the regression through the SIM selection technology and captures the nonlinear risk spillover relationships between real estate companies in a complex higher-dimensional environment, thereby overcoming the limitations of existing models such as high-dimensional interconnection and multivariate variable selection. Second, this paper uses the block model from social network theory to investigate the aggregation, trigger mechanism, and propagation path of risk spillover in the real estate market. Third, this paper discusses the formation mechanism of real estate market risks from the micro-level of companies, which is in line with the actual needs of real estate companies facing a capital dilemma and deeper supply-side regulation in this real estate cycle. The findings provide a useful reference for multi-level real estate market supervision and regulation, and for promoting the healthy development of the real estate industry.
张伟平, 曹廷求. 中国房地产企业间系统性风险溢出效应分析——基于尾部风险网络模型[J]. 金融研究, 2022, 505(7): 94-114.
ZHANG Weiping, CAO Tingqiu. Analysis of Systemic Risk Spillover Effects among China's Real Estate Companies Based on the Tail Risk Network Model. Journal of Financial Research, 2022, 505(7): 94-114.
Acharya, V., L. H. Pedersen, T. Philippon, et al., 2017. Measuring Systemic Risk, Review of Financial Studies, 30(1): 2～47.
[18]
Adrian, T., and M. K. Brunnermeier, 2016. CoVaR, American Economic Review, 106: 1705～1741.
[19]
Billio, M., M. Getmansky, A.W. Lo, et al., 2012. Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors, Journal of Financial Economics, 104(3), 535～559.
[20]
Billio, M., W. L. Andrew, P. Loriana, et al., 2021. Global Realignment in Financial Market Dynamics: Evidence from ETF Networks, SAFE Working Paper, No. 304, Pages 37.
[21]
Diebold, F. X., and K. Yilmaz, 2014. On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms, Journal of Econometrics, 182: 119～134.
[22]
Fan, J., and R. Li, 2001. Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Oroperties. Journal of the American statistical Association, 96(456): 1348～1360.
[23]
Hrdle, W. K., W. N. Wang, and L. N. Yu, 2016. TENET: Tail-event Driven NETwork Risk, Journal of Econometrics, 192: 499～513.
[24]
Hautsch N., J. Schaumburg, and M. Schienle, 2015. Financial Network Systemic Risk Contributions, Review of Finance, 19(2): 685～738.
[25]
Hoesli, M., and K. Reka, 2013. Volatility Spillovers, Comovements and Contagion in Securitized Real Estate Markets, Journal of Real Estate Finance and Economics, 47(1): 1～35.
[26]
Khandani, A. E., A. W. Lo, and R. C. Merton, 2013. Systemic Risk and the Refinancing Ratchet Effect. Journal of Financial Economics, 108(1): 29～45.
[27]
Li X. F., M. H. Sun, and K. Boersma, 2019. Policy Spillover and Regional Linkage Characteristics of the Real Estate Market in China's Urban Agglomerations, Journal of Management Science and Engineering, 4:189～210.
[28]
Mian, A., and A. Sufi, 2009. Home Equity-Based Borrowing, and the US Household Leverage Crisis. American Economic Review, 101 (5): 2132～56.
[29]
Zhang, W., P., X. T. Zhuang, J. Wang, et al., 2020. Connectedness and Systemic Risk Spillovers Analysis of Chinese Sectors based on Tail Risk Network. The North American Journal of Economics and Finance, 101248.
[30]
Zhu, B., R. Fuss, and N. B. Rottke, 2013. Spatial Linkages in Returns and Volatilities among U.S Regional Housing Markets, Real Estate Economics, 41(1): 29～64.
