英文标题：
《Detection of Chinese Stock Market Bubbles with LPPLS Confidence
Indicator》
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作者：
Min Shu, Wei Zhu
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最新提交年份：
2019
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英文摘要：
We present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018. We account for the damping condition of LPPLS model in the search space and implement the stricter filter conditions for the qualification of the valid LPPLS fits by taking account of the maximum relative error, performing the Lomb log-periodic test of the detrended residual, and unit-root tests of the logarithmic residual based on both the Phillips-Perron test and Dickey-Fuller test to improve the performance of LPPLS confidence indicator. Our analysis shows that the LPPLS detection strategy diagnoses the positive bubbles and negative bubbles corresponding to well-known historical events, implying the detection strategy based on the LPPLS confidence indicator has an outstanding performance to identify the bubbles in advance. We find that the probability density distribution of the estimated beginning time of bubbles appears to be skewed and the mass of the distribution is concentrated on the area where the price starts to have an obvious super-exponentially growth. This study is the first work in the literature that identifies the existence of bubbles in the Chinese stock market using the daily data of CSI 300 index with the advance bubble detection methodology of LPPLS confidence indicator. We have shown that it is possible to detect the potential positive and negative bubbles and crashes ahead of time, which in turn limits the bubble sizes and eventually minimizes the damages from the bubble crash.
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中文摘要：
我们利用2002年1月至2018年4月沪深沪深300指数的每日数据，提出了一种基于对数周期幂律奇异性（LPPLS）置信指标的提前泡沫检测方法，用于中国股市正负泡沫的早期因果识别。我们在搜索空间中考虑了LPPLS模型的阻尼条件，并通过考虑最大相对误差，执行去趋势残差的Lomb对数周期测试，实现了更严格的过滤条件，以确认有效的LPPLS拟合，并基于Phillips-Perron检验和Dickey-Fuller检验对对数残差进行单位根检验，以提高LPPLS置信度指标的性能。我们的分析表明，LPPLS检测策略可以诊断与已知历史事件相对应的正气泡和负气泡，这意味着基于LPPLS置信度指标的检测策略在提前识别气泡方面具有优异的性能。我们发现，估计泡沫开始时间的概率密度分布似乎是偏斜的，分布的质量集中在价格开始有明显超指数增长的区域。本研究是文献中首次利用沪深300指数的日数据和LPPLS置信指数的提前泡沫检测方法来识别中国股市泡沫的存在。我们已经证明，有可能提前检测到潜在的正负气泡和碰撞，这反过来又限制了气泡的大小，并最终将气泡碰撞造成的损害降至最低。
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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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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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