Abstract
Accurately forecasting loss given default (LGD) poses challenges, due to its highly skewed distributions and complex nonlinear dependencies with predictors. To this end, we propose a multi-view locally weighted regression (MVLWR) method for LGD forecasting. To address the complexity of LGD distributions, we build a specific ensemble LGD forecasting model tailored for each new sample, providing flexibility and relaxing reliance on distribution assumptions. To address complex relationships, we combine multi-view learning and ensemble learning for LGD modeling. Specifically, we divide original features into multiple complementary groups, build a view-specific locally weighted model for each group, and aggregate the outputs from all view-specific models. An empirical evaluation using a real-world dataset shows that the proposed method outperforms all the benchmarked methods in terms of both out-of-sample and out-of-time performance in LGD forecasting. We also provide valuable insights and practical implications for stakeholders, particularly financial institutions, to enhance their LGD forecasting capabilities.
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