What charge-off rates are predictable by macroeconomic latent factors?
利用美国十大银行控股公司的贷款核销率数据，结合多种降维方法提取宏观经济潜在因子，构建因子增强预测模型，发现该模型对商业和房地产贷款核销率的预测优于基准模型，但对消费贷款预测仍困难。
Charge-offs provide critical insights into the risk level of loan portfolios within the banking system, signaling potential systemic risks that could lead to deep recessions. Utilizing consolidated financial statements, we have compiled the net charge-off rate (COR) data from the 10 largest U.S. bank holding companies (BHCs) for disaggregated loans, including business loans, real estate loans, and consumer loans, as well as the average COR for each loan category among these top 10 banks. We propose factor-augmented forecasting models for CORs that incorporate latent common factor estimates, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models have demonstrated superior performance compared with benchmark forecasting models, particularly well for business loan and real estate loan CORs, while predicting consumer loan CORs remains challenging especially at short horizons. Notably, real activity factors improve the out-of-sample predictability over the benchmarks for business loan CORs even when financial sector factors are excluded.