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We present a semi-static hedging algorithm for callable interest ratederivatives under an affine, multi-factor term-structure model. With atraditional dynamic hedge, the replication portfolio needs to be updatedcontinuously through time as the market moves. In contrast, we propose asemi-static hedge that needs rebalancing on just a finite number of instances.We show, taking as an example Bermudan swaptions, that callable interest ratederivatives can be replicated with an options portfolio written on a basket ofdiscount bonds. The static portfolio composition is obtained by regressing thetarget option's value using an interpretable, artificial neural network.Leveraging on the approximation power of neural networks, we prove that thehedging error can be arbitrarily small for a sufficiently large replicationportfolio. A direct, a lower bound, and an upper bound estimator for therisk-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 severalnumerical experiments.

A semi-static replication approach to efficient hedging and pricing of callable IR derivatives