论文标题
通过新的定位方法和基于网络的分析评估条件协方差估计
Evaluating conditional covariance estimates via a new targeting approach and a networks-based analysis
论文作者
论文摘要
在文献中，对动态变化的协方差的建模和预测引起了很多关注。使用的两个最广泛的条件协方差和相关模型是BEKK和DCC。在本文中，我们推进了一种新方法，以在两种模型中介绍定位，以估计与财务时间序列相关的矩阵。我们的方法基于金融市场中高度相关的资产的特定组，随着时间的流逝，这些关系仍然没有改变。基于估计的参数，我们通过参考文献和网络分析中引入的两个众所周知的损失函数来评估模拟序列的定位方法。我们发现相关图中的所有最大集团以评估我们方法的有效性。经验案例研究的结果令人鼓舞，主要是当资产数量不大时。
Modeling and forecasting of dynamically varying covariances have received much attention in the literature. The two most widely used conditional covariances and correlations models are BEKK and DCC. In this paper, we advance a new method to introduce targeting in both models to estimate matrices associated with financial time series. Our approach is based on specific groups of highly correlated assets in a financial market, and these relationships remain unaltered over time. Based on the estimated parameters, we evaluate our targeting method on simulated series by referring to two well-known loss functions introduced in the literature and Network analysis. We find all the maximal cliques in correlation graphs to evaluate the effectiveness of our method. Results from an empirical case study are encouraging, mainly when the number of assets is not large.
