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Are the stock and real estate markets integrated in China?

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Abstract

This paper examines the dynamic short-run and long-run co-movement between the real estate and stock markets in China by employing a continuous wavelet method. We use gross domestic product and M2 (broad money supply) as control variables to eliminate the common factors of the two markets and to identify the real nexus between them. The empirical results show that the co-movement between real estate and stock prices is weak in the short run, except during the financial crisis period. Since the stock market is highly volatile, while real estate prices are relatively stable, the two markets are less correlated in the short run. The results also show that real estate prices affect stock prices in the long run, which supports the existence of a credit-price effect in China. Real estate prices remained very high in most time periods. Enterprises and individuals can obtain funds from bank loans to invest in the stock market, thus raising stock prices. These findings indicate that the two markets are generally segmented in the short run but are integrated in the long run. The stabilization of the real estate market is critical for stability in the stock market, but not vice versa. Additionally, investments in the two markets may not provide a high level of risk dispersion in the long run in China.

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Correspondence to Xiao-Cui Yin.

Appendix

Appendix

Augmented Dickey-Fuller (1981, ADF), Phillips and Perron (1988, PP) and Kwiatowski et al. (1992, KPSS) tests are employed to examine the stationarity of real estate prices (RP) and stock prices (SP). The corresponding results are shown in Table 2, and we find that the level series is not stationary under the ADF, PP and KPSS tests. However, their first differences (DSP, DRP) are stationary, which means that they are integrated of order 1.

Table 2 Unit root test results of level series and differenced series

To investigate the long-run relationship between the real estate and stock markets in China, we use Johansen cointegration tests in which the optimal lag length is chosen based on the Schwarz information criterion. The results are presented in Table 3. The cointegration test includes both the trace and maximum eigenvalue statistics tests. The results of both tests reported in Table 3 indicate no evidence of a long-run relationship between RP and SP. However, when we control for GDP and M2, we find a co-integration relationship, which is consistent with our empirical results in Figs. 3 and 4. This result means that RP and SP are cointegrated in a long-run relationship.

Table 3 Cointegration tests

Furthermore, we use the vector error correction (VEC) model to obtain the long-run and short-run relationships between RP and SP when GDP and M2 are controlled, respectively. The cointegration and error correction equations are shown in Tables 4 and 5. From the cointegration equations, we find that when M2 is controlled, the cointegration coefficient is larger than the coefficient when GDP is controlled, and this also supports the results of the previous analysis. With regard to the series in logarithmic form, the estimated coefficients represent long-run elasticities. Therefore, when M2 is controlled, the influence of RP on SP is stronger than in the case when GDP is controlled. In addition, RP is nonsignificant in Table 4, which may be because it clearly affects SP only after 2005, as we observe in Fig. 3. Finally, based on the error correction equations, we find that the estimated coefficients of the lagged DRP and DSP are nonsignificant in both Tables 4 and 5, which indicates that there is no obvious nexus in the short run between the two series.

Table 4 Cointegration equation and error correction equation when GDP is controlled
Table 5 Cointegration equation and error correction equation when M2 is controlled

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Su, CW., Yin, XC., Chang, HL. et al. Are the stock and real estate markets integrated in China?. J Econ Interact Coord 14, 741–760 (2019). https://doi.org/10.1007/s11403-018-0215-x

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