摘要翻译：
对数周期幂律(LPPL)模型结合了理性预期泡沫的经济理论、(ii)投资者和交易者模仿和羊群行为金融学以及(iii)分岔和相变的数学和统计物理，成为一种灵活的泡沫检测工具。LPPL模型将加速振荡修饰的资产价格的快于指数的（具有有限时间奇异性的幂律）增长作为泡沫的主要诊断。它体现了一个高回报预期的正反馈循环，与崩盘预期的负反馈螺旋相竞争。我们小组最近进行的两次实际预测说明了LPPL模型的威力：2008年7月初油价泡沫达到顶峰，以及2009年8月初上海股市泡沫破裂。然后我们提出了“负气泡”的概念，它是正气泡的镜像。我们认为，类似的正面反馈也在起作用，助长了这些加速下行的价格螺旋。我们将LPPL模型应用于这些负泡沫，并实现了一种模式识别方法来预测负泡沫的结束时间，负泡沫的特征是反弹（与标准正泡沫相关的崩溃镜像）。由误差图量化的样本外检验显示了预测性能的高度显著性。
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英文标题：
《Diagnosis and Prediction of Tipping Points in Financial Markets: Crashes
  and Rebounds》
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作者：
Wanfeng Yan, Ryan Woodard, Didier Sornette
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最新提交年份：
2010
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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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：
  By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the log-periodic power law (LPPL) model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. The power of the LPPL model is illustrated by two recent real-life predictions performed recently by our group: the peak of the Oil price bubble in early July 2008 and the burst of a bubble on the Shanghai stock market in early August 2009. We then present the concept of "negative bubbles", which are the mirror images of positive bubbles. We argue that similar positive feedbacks are at work to fuel these accelerated downward price spirals. We adapt the LPPL model to these negative bubbles and implement a pattern recognition method to predict the end times of the negative bubbles, which are characterized by rebounds (the mirror images of crashes associated with the standard positive bubbles). The out-of-sample tests quantified by error diagrams demonstrate the high significance of the prediction performance.
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PDF链接：
https://arxiv.org/pdf/1001.0265