论文标题
衡量财务风险的AI方法
An AI approach to measuring financial risk
论文作者
论文摘要
AI人工智能带来了新的定量技术来评估经济状况。在这里，我们描述了系统风险的新措施：财务风险计（FRM）。该度量基于线性分位套索回归的惩罚参数（LAMBDA）。 FRM是通过在美国100个公开交易的金融机构中获得惩罚参数的平均值来计算的。我们通过将提出的FRM与其他系统风险（例如VIX，SRISK和GOOGLE趋势）进行比较，证明了这种基于AI的风险措施的适用性。我们发现，FRM和这些措施之间存在相互的Granger因果关系，这表明FRM作为系统性风险度量的有效性。该项目的实现是使用并行计算进行的，这些代码在使用关键字frm的www.quantlet.de上发布。 R软件包风险分析是另一种工具，目的是整合和促进FRM项目的研究，计算和分析方法。可视化和最新的FRM可以在hu.berlin/frm上找到。
AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (lambda) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly traded financial institutions. We demonstrate the suitability of this AI based risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on hu.berlin/frm.
