论文标题
在金融市场学习无方向的图
Learning Undirected Graphs in Financial Markets
论文作者
论文摘要
我们从金融市场数据的角度研究了Laplacian结构性约束下学习无向图形模型的问题。我们表明，拉普拉斯（Laplacian）约束具有与市场指数因子以及股票之间的条件相关性有关的有意义的物理解释。这些解释导致一组准则，用户在估计金融市场的图表时应意识到这些准则。此外，我们提出了算法来学习无方向的图表，以说明非平稳性和库存集群等财务数据的风格化事实和任务。
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 the 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.
