英文标题：
《Linear and nonlinear market correlations: characterizing financial
  crises and portfolio optimization》
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作者：
Alexander Haluszczynski, Ingo Laut, Heike Modest and Christoph R\\\"ath
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最新提交年份：
2017
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英文摘要：
  Pearson correlation and mutual information based complex networks of the day-to-day returns of US S&P500 stocks between 1985 and 2015 have been constructed in order to investigate the mutual dependencies of the stocks and their nature. We show that both networks detect qualitative differences especially during (recent) turbulent market periods thus indicating strongly fluctuating interconnections between the stocks of different companies in changing economic environments. A measure for the strength of nonlinear dependencies is derived using surrogate data and leads to interesting observations during periods of financial market crises. In contrast to the expectation that dependencies reduce mainly to linear correlations during crises we show that (at least in the 2008 crisis) nonlinear effects are significantly increasing. It turns out that the concept of centrality within a network could potentially be used as some kind of an early warning indicator for abnormal market behavior as we demonstrate with the example of the 2008 subprime mortgage crisis. Finally, we apply a Markowitz mean variance portfolio optimization and integrate the measure of nonlinear dependencies to scale the investment exposure. This leads to significant outperformance as compared to a fully invested portfolio.
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中文摘要：
为了研究股票的相互依赖性及其性质，我们构建了1985年至2015年间美国标准普尔500指数股票日常收益的皮尔逊相关和互信息复杂网络。我们表明，这两个网络都检测到了质量差异，尤其是在（最近）动荡的市场时期，因此表明在不断变化的经济环境中，不同公司的股票之间存在着剧烈波动的相互联系。使用替代数据导出了非线性依赖强度的度量，并在金融市场危机期间得出了有趣的观察结果。与危机期间依赖性主要减少为线性相关性的预期相反，我们表明（至少在2008年危机中）非线性效应显著增加。正如我们以2008年次贷危机为例所证明的那样，网络中的中心性概念有可能被用作异常市场行为的某种预警指标。最后，我们应用Markowitz均值-方差投资组合优化，并整合非线性依赖性度量来衡量投资敞口。与完全投资的投资组合相比，这导致了显著的跑赢大市。
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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：Physics        物理学
二级分类：Chaotic Dynamics        混沌动力学
分类描述：Dynamical systems, chaos, quantum chaos, topological dynamics, cycle expansions, turbulence, propagation
动力系统，混沌，量子混沌，拓扑动力学，循环展开，湍流，传播
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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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