论文标题
强大的债券投资组合通过凸形concave鞍点优化
Robust Bond Portfolio Construction via Convex-Concave Saddle Point Optimization
论文作者
论文摘要
可以通过其对屈服曲线上的点的敏感性来估计，可以估计，在一组产量曲线和扩散的凸曲线和扩散的最小值（最坏情况下）值。我们表明，基于灵敏度的估计值是保守的，即\ ie，低估了最坏的病例值，并且可以通过求解可处理的凸优化问题来找到确切的最坏情况值。然后，我们展示了如何使用凸 - 孔隙鞍点优化构建一个长期的债券组合，其中包含其目标或约束中最坏情况的值。
The minimum (worst case) value of a long-only portfolio of bonds, over a convex set of yield curves and spreads, can be estimated by its sensitivities to the points on the yield curve. We show that sensitivity based estimates are conservative, \ie, underestimate the worst case value, and that the exact worst case value can be found by solving a tractable convex optimization problem. We then show how to construct a long-only bond portfolio that includes the worst case value in its objective or as a constraint, using convex-concave saddle point optimization.
