An Empirical Investigation of Affine Term Structure Model Uncertainty

  • Jing Li Jing Li

For computational tractability reasons, one typically prefers an easy-to-implement model to a complicated one in affine term structure modeling, although the latter might provide a better empirical fit. However, working with a simple nominal model could introduce a significant amount of model uncertainty. One possible approach to evaluate this model uncertainty is to complement a simple choice by an uncertainty set, such that at least empirically relevant models are included. Based on the Kullback-Leibler divergence, it turns out that we are able to obtain in an easy way the largest divergence of the uncertainty set which helps us to quantify the impact of the model uncertainty. This paper illustrates this approach using a class of affine term structure models including more advanced but empirically relevant ones, and compares the uncertainty impacts of several simple models.

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