Abstract: In recent years, the real estate industry is an important engine of Chinese economy. The economy has achieved sustained high-speed growthdriven bythe real estate industry. Meanwhile, the money supply (M2) has been growing at an average annual rate of 15.84 percent, which has also raised concerns about inflation. In this context, whether real estate stock investment can hedge inflation is a very concerned issue for investors.
To test the positive and negative synergistic effect between the real estate stock index return and inflation, AR(m)-EGARCH(p, q)-GED is employed to model the edge distribution of financial time series, and a correlation measurement model is constructed based on Markov-switching GRG copula. In this model, Markov state transformation is mainly used to control the parameters of correlation between two different states. GRG copula does not fix the weight coefficient of mixed copula, which can more flexibly connect the edge distribution according to the data characteristics. This correlation measurement model can not only measure the instantaneous tail correlation between Chinese real estate stock index return and inflation, but also measure the correlation based on quantilebetween them in non-extreme cases.
The empirical results show that from January 2001 to December 2017, there exist two positive and negative structural states of Markov transformation between the real estate stock index return and inflation. Among them, the positive correlation structure between the two is the main state of the economic system. In this state, real estate stock investment has a certain ability to hedge inflation. In the negative correlation dependent structure, real estate stock investment can not hedge inflation. In addition, there exist significant differences in the intensity of correlation between extreme and non-extreme cases. In the main state, the positive correlation between them is stronger in the non-extreme condition than that in the extreme condition. Moreover,in the quantile with the same distance on both sides of _α=0.5, the correlation between the two rises is higher than that between the two falls.
The correlation measure model based on Markov-switching GRG copula can be extended to more application fields to analyze the nonlinear correlation between two time series.The empirical results will provide some useful enlightenments for the formulation of macroeconomic policies and the establishment of investment decisions.

Key words: real estate stock index, inflation rate, Markov-switching, GRG copula, correlation