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Investigating the Dependence Structure between Real Estate and Finance Industries Based on the Copula Function

LIU Qiong-fang1,2,ZHANG Zong-yi1(1.School of Economics and Business Administration,Chongqing University,Chongqing 400044,China;2.School of Mathematics and Statistics,Chongqing University,Chongqing 400044,China)  
Real estate markets and financial markets play essential roles for the continuous growth of developing economies.The development of these two markets has an interrelated influence on one other.Recent studies show that real estate and financial markets are linearly correlated with each other.However,the nonlinear Graner causality between these two markets does not exist.Most of these studies are qualitative and have paid little attention to using stock data to analyze the dependence structure between the real estate and financial industries.This paper applies the Copulas theory to investigate the dependence structure between real estate and financial industries.The Copula theory can capture nonlinear,asymmetric and tail dependence.Our analysis is based on the stock database of Tsinghua Financial Data from July 2,2001 to August 5,2009.We are able to use 1,953 pairs of data.Univariate distribution method is based on a symmeparametric extreme value model.EV Copulas,Archimedean Copulas and Archimax Copulas simulate the correlation between real estate and financial markets.The first part of this study estimates the parameters of the Copula function.We adopt the margin inference method by estimating the parameters for the univariate marginal distribution and for the Copula function.The computation of the Kurtosis method shows that the threshold values are the same for both the univariate distribution and the Generalized Pareto distribution(GPD).Diagnostic plots of the GPD fit are Excess Distribution,Tail of Underlying Distribution,Scatter Plot of Residuals and Record Development.These plots indicate that GPD appears to fit the distribution of threshold excess fairly well.The second part discusses the rationale of selecting the Coupla function.Based on AIC and BIC minimum theories,the Gumbel Copula function shows that a correlation between these two markets exists in only upper tail for single parameter Copulas.However,the BB3 Copula has a higher correlation in the lower tail than the upper tail for a variety of parameters used in the Copula function.These findings illustrate that two different stock returns are more likely to correlate with each other during market downturns than upturns.These findings need careful interpretations if selected Copulas with one or two parameters need to be simulated simultaneously.In summary,the dependence patterns between the financial time series vary with the Copula functions.The GPD model needs to estimate the threshold values in order to exactly fit the margin distribution of Copula functions.In addition,creating an investment portfolio of two different stocks won't necessarily help reduce investment risk.
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