Autoregressive conditional heteroscedasticity (ARCH) effects make the probability of large losses greater than standard meanvariance analysis for investor in shortterm. Capturing ARCH effects accurately is meaningful for the research of shortterm trend of housing price. This paper empirically research and compare the regressive model, GARCH model and AR model with the data of average housing price in China and four municipalities' housing price. The results show the ARCH effects in housing markets in Beijing, Shanghai, Tianjin except Chongqing. We also found out that the regressive model is a little better than GARCH model for shorttime forecast while the GARCH model is better in longterm forecast. Therefore, if it is hard to obtain relative data, GARCH model is more applicable.
徐轲, 马永开, 邓长荣. 中国住房价格波动聚集性研究及短期预测[J]. J4, 2010, 7(6): 943-.
XU Ke, MA Yong-Kai, DENG Chang-Rong. Volatility Clustering and Short-term Forecast of China House Price. J4, 2010, 7(6): 943-.