A Generalized Endogenous Grid Method for Default Risk Models
将内生网格法扩展到违约风险模型，比网格搜索快4到27倍且更精确，应用于Arellano(2008)模型时预测的利差标准差低三分之一、违约频率低3到5倍。
Abstract We extend the endogenous grid method to default risk models, which is faster and more accurate than grid search. Our method is 4 to 27 times faster and provides a more accurate bond price function, resulting in substantial differences in the predictions of the canonical sovereign debt model. When applied to Arellano's (2008) model, our approach predicts a standard deviation of the interest rate spread one‐third lower and defaults 3 to 5 times less frequently than does the conventional approach. Finally, we demonstrate that our method is applicable to a broad class of default risk models by characterizing sufficient conditions.