Positive forward rates in the maximum smoothenss framework
提出一种非线性动态规划算法，在最大平滑框架下计算远期利率并强制其非负，利用瑞典债券市场数据验证了约束的必要性及预测精度的提升。
In this paper we present a nonlinerar dynamic programming algorithm for the computation of forward rates within the maximum smoothness framework. The algorithm implements the forward rate positivity constraint for a one-parametric family of smoothness measures and it handles price spreads in the constraining data set. We investigate the outcome of the algorithm using thw Swedish Bond market showing examples where the absence of the positive constraint leads to negative interest rates. Furthermore we investigate the predictive accuracy of the algorithm as we move along the family of smoothness measures. Amon other things we onserve that the inclusion of spreads not only improves the smoothness of forward curves but also significantly reduces the predictive error.