英文标题：
《Networks, Dynamic Factors, and the Volatility Analysis of
High-Dimensional Financial Series》
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作者：
Matteo Barigozzi and Marc Hallin
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最新提交年份：
2016
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英文摘要：
We consider weighted directed networks for analysing, over the period 2000-2013, the interdependencies between volatilities of a large panel of stocks belonging to the S\\&P100 index. In particular, we focus on the so-called {\\it Long-Run Variance Decomposition Network} (LVDN), where the nodes are stocks, and the weight associated with edge $(i,j)$ represents the proportion of $h$-step-ahead forecast error variance of variable $i$ accounted for by variable $j$\'s innovations. To overcome the curse of dimensionality, we decompose the panel into a component driven by few global, market-wide, factors, and an idiosyncratic one modelled by means of a sparse vector autoregression (VAR) model. Inversion of the VAR together with suitable identification restrictions, produces the estimated network, by means of which we can assess how {\\it systemic} each firm is.~Our analysis demonstrates the prominent role of financial firms as sources of contagion, especially during the~2007-2008 crisis.
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中文摘要：
我们考虑加权有向网络，以分析2000-2013年期间，属于S\\&P100指数的一大类股票的波动性之间的相互依赖性。特别是，我们关注所谓的{\\it Long Run Variance Decomposition Network}（LVDN），其中节点是股票，与edge$（i，j）$相关的权重代表变量$j$的创新所占变量$i$的$h$-步进预测误差方差的比例。为了克服维度诅咒，我们将面板分解为一个由几个全球、市场范围内的因素驱动的组件，以及一个通过稀疏向量自回归（VAR）模型建模的特殊组件。VAR的反转以及适当的识别限制，产生了估计的网络，通过它我们可以评估每个公司的{\\it systemic}~我们的分析表明，金融公司在传染源中扮演着重要角色，尤其是在~2007-2008年危机期间。
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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：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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