Learning Undirected Graphs in Financial Markets
Learning Undirected Graphs in Financial Markets
复制标题
学习金融市场中的无向图
DOI：
10.1109/ieeeconf51394.2020.9443573
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
D. Palomar
中科院分区：
文献类型：
--
作者：
J. Cardoso;D. Palomar
本文从金融市场数据的角度研究了拉普拉斯结构约束下无向图模型的学习问题。我们证明了拉普拉斯约束对市场指数因子和股票之间的条件相关性具有有意义的物理解释。这些解释导致了一组指导方针，用户在估计金融市场中的图形时应该了解这些指导方针。此外，我们提出了学习无向图的算法，这些算法可以解释金融数据固有的风格化事实和任务，如非平稳性和股票聚类。
We investigate the problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market data. We show that Laplacian constraints have meaningful physical interpretations related to the market index factor and to conditional correlations between stocks. Those interpretations lead to a set of guidelines that users should be aware of when estimating graphs in financial markets. In addition, we propose algorithms to learn undirected graphs that account for stylized facts and tasks intrinsic to financial data such as non-stationarity and stock clustering.
投资组合选择总积极性下的协方差矩阵估计*
影响因子：
2.5
作者：
Agrawal, Raj;Roy, Uma;Uhler, Caroline
通讯作者：
Uhler, Caroline