中国住房价格波动聚集性研究及短期预测  

Volatility Clustering and Short-term Forecast of China House Price

作　　者：徐轲[1] 马永开[1] 邓长荣[1] 

机构地区：[1]电子科技大学经济与管理学院

出　　处：《管理学报》2010年第6期943-948,共6页Chinese Journal of Management

基　　金：国家自然科学基金资助项目(70672104);新世纪优秀人才支持计划资助项目(NCET-05-0811)

摘　　要：相对于传统的均值-方差分析,ARCH效应的存在将会使投资者在短期内面临更大的风险。有效地捕捉住房价格ARCH效应对研究住房价格短期走势有重要的实践意义。将长短2个时段样本运用回归模型、GARCH模型、AR模型对中国住房均价及四大直辖市数据进行实证,结果表明:在2个时段上我国住房价格均存在ARCH效应,除重庆外其他3个直辖市也存在ARCH效应;此外,在短时段的预测上,回归模型略优于GARCH模型,而GARCH模型在长时段预测效果上要优于回归模型。可见,在相关数据难找的情况下,GARCH模型是切实可行的短期预测方法。Autoregressive conditional heteroscedasticity（ARCH） effects make the probability of large losses greater than standard mean-variance analysis for investor in short-term.Capturing ARCH effects accurately is meaningful for the research of short-term 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 short-time forecast while the GARCH model is better in long-term forecast.Therefore,if it is hard to obtain relative data,GARCH model is more applicable.

关 键 词：住房价格 波动聚集性 时序分析 GARCH模型 短期预测 

分 类 号：C93[经济管理—管理学]