This paper presents a factor-based forecasting model for the financial market vulnerability, measured by changes in the Cleveland Financial Stress Index (CFSI). We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data in order to out-of-sample forecast the CFSI. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability at least for the short-term forecast horizons, which is a desirable feature since financial crises often come to a surprise realization. Interestingly, the first common factor, which plays a key role in predicting the financial vulnerability index, seems to be more closely related with real activity variables rather than nominal variables. We also present a binary choice version factor model that estimates the probability of the high stress regime successfully.

中文翻译：

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预测美国的金融脆弱性：因子模型方法

本文提出了一种基于因子的金融市场脆弱性预测模型，通过克利夫兰金融压力指数 (CFSI) 的变化来衡量。为了对CFSI进行样本外预测，我们通过170个月频宏观经济数据的主成分法估计潜在公因子。至少在短期预测范围内，我们的因子模型在样本外可预测性方面的表现优于随机游走和自回归基准模型，这是一个理想的特征，因为金融危机通常会突然出现。有趣的是，在预测金融脆弱性指数中起关键作用的第一个公因子似乎与实际活动变量而非名义变量的关系更为密切。