摘要翻译：
在经济时间序列的高维面板中，在水平或收益上具有动态因素结构的挥发，通常也允许动态因素分解。我们考虑了一个两阶段的动态因子模型方法来恢复水平和对数挥发物的共同成分和特殊成分。具体地说，在第一个估计步骤中，我们提取水平的共同和特殊冲击，从中计算对数波动率代理。在第二步中，我们估计了一个动态因子模型，它等价于对数波动率面板的乘法因子结构。通过利用这种两阶段因子方法，我们建立了一步前条件预测区间的大$N倍T$回报面板。这些区间是基于经验分位数，而不是基于条件方差；它们可以是等尾的，也可以是不等尾的。当$N$和$T$都趋于无穷大时，我们给出了所提出的估计量的一致相合性和相合率的结果。我们用蒙特卡罗模拟方法研究了估计量的有限样本性质。最后，我们将我们的方法应用于一个属于S&P100指数的资产收益面板，以计算2006-2013年期间的一步提前条件预测区间。与componentwise GARCH基准（它不利用横截面信息）的比较表明了我们的方法的优越性，它是真正的多变量（和高维）、非参数和无模型的。
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英文标题：
《Generalized Dynamic Factor Models and Volatilities: Consistency, rates,
  and prediction intervals》
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作者：
Matteo Barigozzi and Marc Hallin
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Volatilities, in high-dimensional panels of economic time series with a dynamic factor structure on the levels or returns, typically also admit a dynamic factor decomposition. We consider a two-stage dynamic factor model method recovering the common and idiosyncratic components of both levels and log-volatilities. Specifically, in a first estimation step, we extract the common and idiosyncratic shocks for the levels, from which a log-volatility proxy is computed. In a second step, we estimate a dynamic factor model, which is equivalent to a multiplicative factor structure for volatilities, for the log-volatility panel. By exploiting this two-stage factor approach, we build one-step-ahead conditional prediction intervals for large $n \times T$ panels of returns. Those intervals are based on empirical quantiles, not on conditional variances; they can be either equal- or unequal- tailed. We provide uniform consistency and consistency rates results for the proposed estimators as both $n$ and $T$ tend to infinity. We study the finite-sample properties of our estimators by means of Monte Carlo simulations. Finally, we apply our methodology to a panel of asset returns belonging to the S&P100 index in order to compute one-step-ahead conditional prediction intervals for the period 2006-2013. A comparison with the componentwise GARCH benchmark (which does not take advantage of cross-sectional information) demonstrates the superiority of our approach, which is genuinely multivariate (and high-dimensional), nonparametric, and model-free.
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PDF链接：
https://arxiv.org/pdf/1811.10045