论文标题
一种半静态复制方法，用于有效的套期保值范围和定价
A semi-static replication approach to efficient hedging and pricing of callable IR derivatives
论文作者
论文摘要
我们提出了一种在仿射的多因素项结构模型下的可调查利率导数的半静态对冲算法。借助传统的动态树篱，随着市场的发展，需要在随着时间的推移中不断更新复制组合。相比之下，我们提出了一个半静态对冲，需要在有限数量的实例上重新平衡。我们以示例为例Bermudan Swaptions，可以通过在一篮子折扣债券上写的期权投资组合来复制可销的利率衍生品。静态投资组合组成是通过使用可解释的人工神经网络回归目标选项值的。利用神经网络的近似能力，我们证明对冲误差可能很小，对于足够大的复制组合可能很小。从对冲算法中推断出风险中立的百慕大交换价格的直接，下限和上限估计器。此外，确定了价格统计数据的闭合误差边距。我们实际上通过几个数值实验来证明对冲和定价性能。
We present a semi-static hedging algorithm for callable interest rate derivatives under an affine, multi-factor term-structure model. With a traditional dynamic hedge, the replication portfolio needs to be updated continuously through time as the market moves. In contrast, we propose a semi-static hedge that needs rebalancing on just a finite number of instances. We show, taking as an example Bermudan swaptions, that callable interest rate derivatives can be replicated with an options portfolio written on a basket of discount bonds. The static portfolio composition is obtained by regressing the target option's value using an interpretable, artificial neural network. Leveraging on the approximation power of neural networks, we prove that the hedging error can be arbitrarily small for a sufficiently large replication portfolio. A direct, a lower bound, and an upper bound estimator for the risk-neutral Bermudan swaption price is inferred from the hedging algorithm. Additionally, closed-form error margins to the price statistics are determined. We practically demonstrate the hedging and pricing performance through several numerical experiments.
