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.

翻译：条件风险价值（CoVaR）通过衡量某一资产在另一资产陷入困境条件下的损失分位数来量化系统性金融风险。我们开发了一种基于Transformer的方法，将金融新闻文章与市场数据直接整合以改进CoVaR估计。与使用预定义情感评分的方法不同，我们的方法融合了由大语言模型（LLM）生成的原始文本嵌入。我们证明了Transformer CoVaR估计量的显式误差界，表明即使在小数据集上也能实现准确的CoVaR学习。利用2006-2013年美国市场收益和路透社新闻数据，我们的样本外结果表明文本信息会影响CoVaR预测。通过更优的预测性能，我们发现当比较基于Transformer的CoVaR与无文本CoVaR及使用传统情感度量的CoVaR时，多个权益资产在市场压力期间均呈现显著的负向低谷。我们的研究证明，文本数据可用于有效建模系统性风险，且无需依赖过大规模的数据集。