Fang published a paper on a survival model for mortgage credit risk incorporating dynamic industrial graphs
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Shiqi Fang released a paper on a discrete-time survival model for mortgage credit risk incorporating dynamic industrial graphs, with backtesting showing its in-sample fit and out-of-sample discrimination outperform static industrial graph models. Addressing the modeling need for cross-regional dependence in mortgage credit risk, the study constructs the model using industrial structure similarity to measure economic proximity, with the dynamic industrial graph reconstructed annually, and topology and edge weights adjusted as regional industrial structures evolve. The model combines conditional autoregressive processes and first-order autoregressive processes, uses integrated nested Laplace approximation for estimation, and completes backtesting based on US loan-level mortgage data.
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- arXiv q-finDiscrete-Time Survival Model Based on Dynamic Industry Graph for Mortgage Credit Risk
To meet the modeling needs of cross-regional dependence in mortgage credit risk, the study proposes measuring economic proximity by industrial structure similarity and constructing a discrete-time survival model with a dynamic industry graph. The dynamic industry graph is reconstructed annually, with topology and edge weights adjusted as regional industrial structures evolve. This model combines a conditional autoregressive process and a first-order autoregressive process, estimated using integrated nested Laplace approximation. Backtesting based on US mortgage loan-level data shows that its in-sample fit and out-of-sample discrimination are superior to static industry graph models.
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