Risk factor aggregation and stress testing
通过无监督学习中的PCA和自编码器技术聚合风险因子，生成全球、区域及行业因子，用于改进压力测试中的风险因子建模，对金融风险管理者和量化分析师有参考价值。
Stress testing refers to the application of adverse financial or macroeconomic scenarios to a portfolio. For this purpose, financial or macroeconomic risk factors are linked with asset returns, typically via a factor model. We expand the range of risk factors by adapting dimension-reduction techniques from unsupervised learning, namely PCA and autoencoders. This results in aggregated risk factors, encompassing a global factor, factors representing broad geographical regions, and factors specific to cyclical and defensive industries. As the adapted PCA and autoencoders provide an interpretation of the latent factors, this methodology is also valuable in other areas where dimension-reduction and explainability are critical.