| Foreign scholars has been researched the real estate industry linkage since the GreatDepress in1930’s. So far these researches are relatively mature. Foreign scholars mainlyfocus on Europe and the United States economic entity and their researchmethod includes input-output method, computable general equilibrium model (CGE) and soon. There is a relatively short history when domestic scholars have studied the real estateindustry linkage and the number of the related literatures is small. A few papers make anempirical research on the domestic or local area real estate industry linkage in China based onreal economic perspective by using different methods. Most literature mainly focuses on therelationship between real estate industry and the national economic growth, however,literature focus on real estate industry linkage in China based on virtual economic perspectiveis little. At present, the research method used by domestic scholars includes input-outputmethod, computable general equilibrium model (CGE), vector autoregressivemodel (VAR) etc. Unfortunately, these methods have their own limitation. For example,input-output method has some limitations that data update speed is slow and can onlycharacterize the linear, static industry linkage. At the same time, input-output method isunable to capture dynamic industry linkage and its structural break.CGE can not only fully describe the static and dynamic relationship between eachdepartment or account among the national economic system, but also can describe thesedepartments’ response to the external shocks. However, the data CGE needed is morecomplex and hard to find than the input-output method. Compared with the input-outputmethod, the VAR model can reveal dynamic impact of real estate industry on related industryand the causality between respective industries; however, the VAR model can only describethe real estate industry linkage and impulse response analysis under a single regime. It cannotcapture the industry linkage and impulse response under different regimes.As for the market risk evolution pattern, the research progress of domestic and foreignscholars is similar after the CAViaR model is formally proposed by Engle and Manganelli in2004. Generally speaking, the scholars mainly made two aspects research work. First is tomake empirical research by directly using CAViaR model. Second is to expand the CAViaRmodel and improve its estimation method. Foreign scholars do better than domestic scholarsin the expansion of CAViaR model. Most scholars make empirical research on the stockmarket and future market based CAViaR model, but less related to the real estate industry.In the light of shortcoming among research perspective and method in existing literature, we take real estate industry in China as study object in this paper. The research aim is toreveal real estate industry association mechanism and its market risk evaluation pattern inChina. We use dynamic copula model to study the time-varying linkage between real estateindustry and finance industry in virtues economic perspective. On the basis, we make analysison the structural break among the dynamic time-varying linkage. We use vine copula to modelthe static dependency structure among the real estate and its related industries in virtueseconomic perspective. Similar, we adopt MS-VAR model to reveal the industry linkagemechanism and impulse response between real estate industry and its related industries underdifferent regimes. Finally, we use CAViaR model to reveal the market risk evolution patternand its transmission mechanism among the real estate industry chain.The conclusions of this study are as follows:First is about the dynamic linkage between real estate industry and finance industry, wefind that dependency between real estate and financial industry lacks continuity. Thedependency shows asymmetry changes as the return changes. With the rise in return, thecorrelation coefficient of both condition lower tail and upper tail decreases, but the decline inthe upper tail dependence coefficient is greater than in the lower tail dependence coefficient.In stock market, the dependency between real estate and financial industry is significantlyhigher in downturn period than in boom period. This shows that the dependency changedepends significantly on the return of the past and there is a reverse relationship. The higherthe volatility of the return of the past, the closer the relationship between the two industries is,and vice versa. There is some structural break among these time-varying linkages betweenreal estate and financial sector. The date of the occurrence of the structural break either isoften accompanied by major policy affecting the stock market, or means the turning point ofstock market trend.Second is about the static linkage among the real estate industry chain. There is complexstatic dependency structure between real estate industry and its related industries such as ironand steel, building materials, decoration, household appliances, and bank. Overall, there issignificant non-conditional dependence among the real estate industry chain. For example, thedependence between the building material industry and decoration industry is up to1.04. Thedependence between real estate industry and bank industry is0.67. There is heavy tailed andasymmetric dependency between iron steel industry and building materials. Similar, buildingmaterials and architectural decoration, decoration and household electrical appliance industryalso exist the same dependency structure whose upper tail is more lager than the lower taildependence. At the same time, there is symmetry upper (lower) dependence types between household electrical appliances and real estate industry, real estate and bank industry. The nonconditional dependences are more larger than conditional dependences among differentindustries.Third is about the real estate industry linkage mechanism and impulse response. There ismutual influence mechanism under different regimes between real estate and relatedindustries (steel, cement, furniture, home appliances). During the study period, the real estateindustry plays an obvious role in promoting the related industries such as steel industry,cement industry and furniture industry. The growth shock from real estate industry can drivethe related industry to grow during the next12months. There are three development regimesin real estate industry during the study period. The first regime represents the unpopularperiod of property market. The second regime represents the recovery period of propertymarket. The third regime represents the downturn period of property market. The averagesustainable time for the first regime is9.53months, while the second regime is20.10monthsand the third regime is15.56months. Therefore, most time of the study period in Chineseproperty market is unpopular and recovery period. The real estate industry linkage showsasymmetric and nonlinear characteristics in different regime. During property downturn, theshock from real estate industry has the largest negative impact on related industries. Duringthe property frenzy, the positive effect on related industries from the real estate shock isrelatively persistent. During property recovery, the positive effect on related industries fromthe real estate shock is relatively short and show shock attenuation trend.Forth is about evolution pattern of real estate market risk. The research result shows thatAS model is the most suitable for modeling the evolution pattern of real estate industry and itsrelated industries. The market risk of real estate industry shows notable self-correlation. Thatis the current VaR of real estate is not only affected significantly by the previous VaR(Value atRisk), but also is affected significantly by the current positive return or negative return. At thesame time, the impact of negative return on the VaR is greater than that of positive returnwhich show the non symmetry shock effect. We believe that the leverage effect is significant.Fifth is about the conduction mechanism of market risk in real estate industry chain. Thereal estate industry is one-way Granger to related industries such as iron and steel, buildingmaterials industry, household electrical appliance and bank industry. Duration of VaR transferis2days for iron and steel,4days for building materials,4days for household electricalappliance, and3days for bank. There is two-way Granger causal relationship between realestate industry and building materials industry which will conduct VaR to each other in4days. Compared with previous studied, the innovation of this paper lies in:First is to expand the perspective to analyze real estate linkage. In this paper, we make anempirical analysis on the linkage between real estate and related industry in China based onthe real economy perspective and the virtual economic perspective. We adopt differentmethod to analysis the static and dynamic industry linkage and its nonlinear, non symmetricalfeatures. On this basis, we try to dig out the structural break and its occurrence time. At thesame time, we further analysis how the external financial policies affect the dynamicdependence between the real estate industry and finance industry and its structural break. Thestudy conclusion enriches the research perspective in real estate industry theory. It can helpgovernment regulatory to understand how the macroeconomic adjustment and othermajor financial policies may affect the real estate industry linkage. Also it helps the real estatebusiness operators and investors to know the real estate linkage characteristic and build moreefficient portfolio.Second is to reveal the real estate industry linkage mechanism under different regimes.Input-output method can only analyze static, linear linkage in real estate industry chain. Alsodifferent from the virtual economy perspective in most studies which ignore the real economicperspective. We use MS-VAR method to reveal the real estate industry linkage mechanismunder different regimes. On this basis, we describe the impulse response of related industry toreal estate industry. The result shows that there is nonlinear, non symmetry interactioncharacteristic in the real estate industry linkage mechanism under different regimes.Third is to describe the evolution pattern of market risk in real estate industry and its riskconduction mechanism. Different from prior to assume a distribution of asset return, wedirectly model the real estate market risk in the quantile perspective and measure the marketrisk by building quantile(VaR) evolution model. This method can not only avoid the errors inassumption about the distribution of asset return, but also can reflect better the non normaldistribution of asset return and get the VaR. What is more important is to explore theautoregressive process in quantile itself and reveal the evolution pattern of the real estatemarket risk. In this paper, we confirmed rationality of CAViaR model by using variousdetection index. After getting the market risk variation(ΔVaR), we analyze the conductionmechanism of market risk between real estate industry and related industry by Grangercausality test.
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