Expected Loan Loss Provisioning: An Empirical Model
开发了一个预测长期贷款损失的模型，结合宏观经济预测，比现有模型更准确，并能更有效检测银行倒闭，用于估计预期损失的现值及其对银行决策的影响。
ABSTRACT The new accounting standard requires that financial institutions estimate expected credit losses on their loan portfolios. The predictability of long-term losses, however, remains an open question. We develop a model that predicts long-term loan losses and incorporates adjustments for macroeconomic forecasts. The model combines cross-sectional predictions with a high-dimensional dynamic factor model that tracks aggregate losses over the business cycle. The model predicts long-term losses out-of-sample with significantly greater accuracy than the Harris et al. (2018) model and several other alternatives. It is also more effective at detecting bank failures. We use the model to estimate the present value of expected losses and the expected loss overhang for a given bank-quarter. The estimated present values subsume information in reported allowances and in fair value disclosures about long-term losses; the evidence is also consistent with loss overhang distorting banks' decisions. The model provides a useful benchmark to study loan loss provisioning. JEL Classifications: G21; M40; M41.