英文标题：
《Characterization of catastrophic instabilities: Market crashes as
  paradigm》
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作者：
Anirban Chakraborti, Kiran Sharma, Hirdesh K. Pharasi, Sourish Das,
  Rakesh Chatterjee and Thomas H. Seligman
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最新提交年份：
2018
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英文摘要：
  Catastrophic events, though rare, do occur and when they occur, they have devastating effects. It is, therefore, of utmost importance to understand the complexity of the underlying dynamics and signatures of catastrophic events, such as market crashes. For deeper understanding, we choose the US and Japanese markets from 1985 onward, and study the evolution of the cross-correlation structures of stock return matrices and their eigenspectra over different short time-intervals or \"epochs\". A slight non-linear distortion is applied to the correlation matrix computed for any epoch, leading to the emerging spectrum of eigenvalues. The statistical properties of the emerging spectrum display: (i) the shape of the emerging spectrum reflects the market instability, (ii) the smallest eigenvalue may be able to statistically distinguish the nature of a market turbulence or crisis -- internal instability or external shock, and (iii) the time-lagged smallest eigenvalue has a statistically significant correlation with the mean market cross-correlation. The smallest eigenvalue seems to indicate that the financial market has become more turbulent in a similar way as the mean does. Yet we show features of the smallest eigenvalue of the emerging spectrum that distinguish different types of market instabilities related to internal or external causes. Based on the paradigmatic character of financial time series for other complex systems, the capacity of the emerging spectrum to understand the nature of instability may be a new feature, which can be broadly applied.
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中文摘要：
灾难性事件虽然罕见，但确实会发生，一旦发生，就会产生毁灭性的影响。因此，了解市场崩盘等灾难性事件的基本动态和特征的复杂性至关重要。为了加深理解，我们从1985年起选择了美国和日本市场，研究了股票收益矩阵的互相关结构及其特征谱在不同的短时间间隔或“时代”上的演化。对任何历元计算的相关矩阵施加轻微的非线性失真，导致出现特征值谱。新兴光谱的统计特性显示：（i）新兴光谱的形状反映了市场的不稳定性，（ii）最小特征值可能能够从统计上区分市场动荡或危机的性质——内部不稳定或外部冲击，（iii）时滞最小特征值与平均市场互相关具有统计显著相关性。最小特征值似乎表明，金融市场变得更加动荡，其方式与平均值相似。然而，我们展示了新兴谱的最小特征值的特征，这些特征区分了与内部或外部原因相关的不同类型的市场不稳定性。基于其他复杂系统金融时间序列的范式特征，新兴谱理解不稳定性本质的能力可能是一个新特征，可以广泛应用。
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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：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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