基于Transformer的CoVaR：文本信息中的系统性风险
Transformer-based CoVaR: Systemic Risk in Textual Information
AI总结：
本文提出基于Transformer的CoVaR方法，利用文本信息提升系统性风险预测，展示在小数据集下也能实现准确估计。
摘要：
条件价值-at-风险（CoVaR）通过衡量一个资产的损失分位数来量化系统性金融风险，该分位数是在另一个资产出现困境时计算的。我们开发了一种基于Transformer的方法，将金融新闻文章直接与市场数据结合，以提高CoVaR估计的准确性。与使用预定义情感评分的方法不同，我们的方法利用大型语言模型（LLM）生成的原始文本嵌入。我们为Transformer CoVaR估计器证明了显式的误差界限，表明即使在小数据集上，准确的CoVaR学习也是可能的。使用美国市场回报率和2006-2013年雷顿新闻条目，我们的离样结果表明，文本信息会影响CoVaR预测。由于更好的预测性能，我们在市场压力时期，比较基于Transformer的CoVaR与不使用文本的CoVaR以及使用传统情感措施的CoVaR时，发现多个股权资产出现显著的负 dip。我们的结果表明，文本数据可以有效地用于建模系统性风险，而无需使用极大数据集。
英文摘要：
Conditional Value-at-Risk (CoVaR) quantifies systemic financial risk by measuring the loss quantile of one asset, conditional on another asset experiencing distress. We develop a Transformer-based methodology that integrates financial news articles directly with market data to improve CoVaR estimates. Unlike approaches that use predefined sentiment scores, our method incorporates raw text embeddings generated by a large language model (LLM). We prove explicit error bounds for our Transformer CoVaR estimator, showing that accurate CoVaR learning is possible even with small datasets. Using U.S. market returns and Reuters news items from 2006--2013, our out-of-sample results show that textual information impacts the CoVaR forecasts. With better predictive performance, we identify a pronounced negative dip during market stress periods across several equity assets when comparing the Transformer-based CoVaR to both the CoVaR without text and the CoVaR using traditional sentiment measures. Our results show that textual data can be used to effectively model systemic risk without requiring prohibitively large data sets.
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