论文标题
市场国家：一种新的理解
Market states: A new understanding
论文作者
论文摘要
我们在2006 - 2019年期间对标准普尔500（美国）和Nikkei 225（JPN）市场的金融市场进行聚类分析，作为复杂系统的一个例子。我们研究了从滑动时期构建的相关矩阵的统计特性。相关矩阵可以根据相关结构的相似性分为不同的集群，称为市场状态。我们将标准普尔500标准指数的市场群分为四个，而Nikkei 225通过优化群内距离的价值，将其归为六个市场。市场显示这些市场国家之间的过渡与向关键市场的过渡的统计特性之间的过渡可能表明可能发生灾难性事件的前体。我们还分析了从市场状态的平均相关性构建的替代数据的相同聚类技术，并且由于短时间序列的白噪声而引起的波动。我们使用相关的Wishart正交合奏来构建替代数据，其平均相关性等于真实数据的平均值。
We present the clustering analysis of the financial markets of S&P 500 (USA) and Nikkei 225 (JPN) markets over a period of 2006-2019 as an example of a complex system. We investigate the statistical properties of correlation matrices constructed from the sliding epochs. The correlation matrices can be classified into different clusters, named as market states based on the similarity of correlation structures. We cluster the S&P 500 market into four and Nikkei 225 into six market states by optimizing the value of intracluster distances. The market shows transitions between these market states and the statistical properties of the transitions to critical market states can indicate likely precursors to the catastrophic events. We also analyze the same clustering technique on surrogate data constructed from average correlations of market states and the fluctuations arise due to the white noise of short time series. We use the correlated Wishart orthogonal ensemble for the construction of surrogate data whose average correlation equals the average of the real data.
