股权资产泡沫驱动因素的实证研究:基于20个国家的证据
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  • 英文篇名:Empirical investigation on the determinants of asset bubbles:Evidence from 20 Economies
  • 作者:陈浪南 ; 王升泉
  • 英文作者:CHEN Lang-nan;WANG Sheng-quan;Lingnan(University) College,Sun Yat-sen University;International School of Business & Finance,Sun Yat-sen University;
  • 关键词:股权资产泡沫 ; 驱动因素 ; 面板Logit模型 ; BMA-Logit模型
  • 英文关键词:asset bubbles;;determinants;;panel data Logit model;;pooled BMA-Logit model
  • 中文刊名:JCYJ
  • 英文刊名:Journal of Management Sciences in China
  • 机构:中山大学岭南(大学)学院;中山大学国际金融学院;
  • 出版日期:2019-01-15
  • 出版单位:管理科学学报
  • 年:2019
  • 期:v.22;No.175
  • 基金:广东省自然科学基金资助项目(2017A030311038);; 教育部人文社会科学研究规划基金资助项目(17YJA790011);; 广东省社科规划课题资助项目(GD17TW01-3)
  • 语种:中文;
  • 页:JCYJ201901001
  • 页数:16
  • CN:01
  • ISSN:12-1275/G3
  • 分类号:6-21
摘要
资产价格泡沫是关系金融、经济能否稳定发展的重要经济现象.现有国内外文献鲜有对资产价格泡沫驱动因素的实证探讨.以理性资产泡沫理论为依据,使用20个代表经济体2000年—2015年的经济金融数据,采用面板Logit模型实证研究了股权资产泡沫的驱动因素,弥补了该领域研究的不足.研究发现,交易量和价格波动率是股权资产泡沫的显著正向驱动因素,但对程度严重的股权泡沫而言,交易量不再是显著的决定因素;货币政策是驱动股权资产泡沫产生的显著因素之一;银行对私人部门的信贷规模及其滞后项也是股权资产泡沫的稳健决定变量.除此之外,还考察了制度变量对股权泡沫发生的作用,基本结论可概括为普通法、小投资者保护度低、政府透明度高、套利自由的国家更易发生股权资产泡沫.最后,还通过BMA-Logit模型和其他方法做了一系列的稳健性检验,确保了研究结论的稳健性.
        Based on the rational bubbles theory,this study examines the determinants of asset bubbles by using the panel data Logit model and the data of 20 Economies dated from 2000 to 2015.The results suggest that trading volume and price volatility are significantly positive determinants of asset bubbles with the full sample.However,the explainable power of trading volume disappears during the period of severe equity bubbles.The results also reveal that monetary policy is a determinant of asset bubbles.Credit and its lag term are the two important variables that drive asset bubbles.Besides,this paper investigates the effect of the institutional variables on the occurrence of equity bubbles.The results suggest that equity bubbles are more likely to occur in the country with common law system,low protection on small investors,high transparency of government and free arbitrage.These results are confirmed by the robustness checks.
引文
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    (2)资产泡沫在驱动因素的选取依据的是理性资产价格泡沫理论,该理论源于Samuelson[2]和Tirole[4]的研究,在此基础上学者做了诸多拓展研究.另外,还有一派泡沫理论的研究范式,即考察行为因素,如市场情绪、过度自信、从群效应[15]等在泡沫形成中的作用.需要说明的是,虽然我们承认行为因素在泡沫形成中会起到关键的作用,但由于至少以下两点原因,本文研究并未将其纳入分析框架,第一,本文是总量分析,个体层面的非理性并不一定导致市场层面的非理性,第二,由于行为因素在泡沫传导中的复杂性,不适合与理性因素在同一框架下分析.基于以上原因,本文的分析限定在理性资产泡沫理论的范畴内.
    (3)在模型设定中,我们不能将时间固定效应考虑进来,因为本文使用的是小N(=20)和大T(=64)的样本,意味着偶发参数问题会在时间维度上存在.另外,由于面板Probit模型无法刻画固定效应,因此本文使用面板Logit模型.
    (4)该方法已经受到很多中央银行等部门的关注,包括美国联邦储备银行、美国财政部、中国人民银行、德国中央银行、加拿大中央银行、韩国中央银行、香港金融管理局、法国中央银行、台湾中央银行、新加坡金融管理局、政府投资公司等(参见:http://www. mysmu. edu/faculty/yujun/).
    (5)2014年—2015年间,S&P500指数达到2000点以上的历史高位,是进入新世纪以来的最高阶段,而这显然不是经济基本面所支撑的,因为2008金融危机后美国实体经济一直处于缓慢复苏的阶段.甚至Market Watch专栏作家Brett Arends曾撰文指出2014年是美国股市历史上的第三大泡沫(参见:http://finance. sina. com. cn/stock/usstock/comment/20140812/023619979148. shtml).
    (6)尽管在本文设定中,时间固定效应可能也是显著的,但出于偶发参数问题的考虑,参考Schularick和Taylor[41]的设定未考虑时间固定效应.
    (7)需要指出的是,消费者增长只是时变折现系数的一个粗糙代理变量,可能并不能完全反映折现系数变动对资产价格异象的解释,需要更加深入的研究.
    (8)法律起源数据来源于La Porta等[44],小投资者保护力度数据来源于世界银行发布的各期《全球营商环境报告》,政府透明度数据来源于透明国际组织的报告,卖空约束数据来源于Gromb和Vayanos[49].
    (9)由于法律制度起源为非时变变量,在面板模型估计时会被删除,因此该处使用了混合Logit模型.

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