摘要翻译：
在标准计量经济学和金融经济学方法中，明确地确定资产价格中存在泡沫仍然是一个尚未解决的问题。问题的很大一部分在于，资产的基本价值通常是不可直接观察的，而且难以计算。此外，不可能区分指数级增长的基本价格和指数级增长的泡沫价格。在Johansen-Ledoit-Sornette(JLS)模型的基础上，我们提出了一系列新的模型。JLS模型是一种灵活的工具，可以检测金融市场中的泡沫，预测金融市场中的制度变化。我们的新模型从泡沫校准中识别资产价格的基本价值和崩溃非线性。除了预测泡沫结束的时间外，新模型还可以估计泡沫的基值和破裂非线性。此外，在新模型中得到的碰撞非线性为可能识别气泡后碰撞动力学提供了一种新的方法。我们使用来自不同市场的三次以崩溃告终的历史泡沫的数据来测试模型。它们是：1997年香港恒生指数暴跌、1987年标准普尔500指数暴跌和2009年上证综指暴跌。结果表明，新模型在描述泡沫、预测泡沫结束时间、估计泡沫的基本值和破裂非线性方面都有很好的表现。在高斯残差假设和非高斯残差假设下对新模型的性能进行了测试。在高斯残差假设下，采用Wilks统计量的嵌套假设，P值表明需要更多参数的模型。在非高斯残差假设下，我们用bootstrap方法得到了假设的I型和II型误差。所有测试都证实了广义JLS模型比标准JLS模型提供了有用的改进。
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英文标题：
《Inferring Fundamental Value and Crash Nonlinearity from Bubble
  Calibration》
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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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英文摘要：
  Identifying unambiguously the presence of a bubble in an asset price remains an unsolved problem in standard econometric and financial economic approaches. A large part of the problem is that the fundamental value of an asset is, in general, not directly observable and it is poorly constrained to calculate. Further, it is not possible to distinguish between an exponentially growing fundamental price and an exponentially growing bubble price. We present a series of new models based on the Johansen-Ledoit-Sornette (JLS) model, which is a flexible tool to detect bubbles and predict changes of regime in financial markets. Our new models identify the fundamental value of an asset price and crash nonlinearity from a bubble calibration. In addition to forecasting the time of the end of a bubble, the new models can also estimate the fundamental value and the crash nonlinearity. Besides, the crash nonlinearity obtained in the new models presents a new approach to possibly identify the dynamics of a crash after a bubble. We test the models using data from three historical bubbles ending in crashes from different markets. They are: the Hong Kong Hang Seng index 1997 crash, the S&P 500 index 1987 crash and the Shanghai Composite index 2009 crash. All results suggest that the new models perform very well in describing bubbles, forecasting their ending times and estimating fundamental value and the crash nonlinearity. The performance of the new models is tested under both the Gaussian and non-Gaussian residual assumption. Under the Gaussian residual assumption, nested hypotheses with the Wilks statistics are used and the p-values suggest that models with more parameters are necessary. Under non-Gaussian residual assumption, we use a bootstrap method to get type I and II errors of the hypotheses. All tests confirm that the generalized JLS models provide useful improvements over the standard JLS model.
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PDF链接：
https://arxiv.org/pdf/1011.5343