摘要翻译：
房地产市场在经济中扮演着至关重要的角色，房地产泡沫的破裂通常会破坏金融体系的稳定，导致经济衰退。本文基于随机矩阵理论(RMT)对美国住房市场（1975-2011）的系统风险和时空动态进行了州级分析。我们在偏离RMT预测的最大特征值中识别出丰富的经济信息，并揭示了特征向量的分量符号要么包含地理信息，要么包含房价增长率差异的程度，或者两者兼而有之。结果表明，美国住宅市场经历了六种不同的状态，这与用盒聚类算法和共识聚类算法在偏相关矩阵上识别出的状态簇的演化是一致的。我们的分析发现，系统性风险的急剧增加通常伴随着制度的转变，这提供了一种早期发现房地产泡沫的手段。
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英文标题：
《Systemic risk and spatiotemporal dynamics of the US housing market》
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作者：
Hao Meng (ECUST), Wen-Jie Xie (ECUST), Zhi-Qiang Jiang (ECUST), Boris
Podobnik (BU and ZSEM), Wei-Xing Zhou (ECUST), H. Eugene Stanley (BU)
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最新提交年份：
2013
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify rich economic information in the largest eigenvalues deviating from RMT predictions and unveil that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. Our results show that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. Our analysis uncovers that dramatic increases in the systemic risk are usually accompanied with regime shifts, which provides a means of early detection of housing bubbles.
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