  American and Bermudan-type financial instruments are often priced with
specific Monte Carlo techniques whose efficiency critically depends on the
effective dimensionality of the problem and the available computational power.
In our work we focus on Bermudan Swaptions, well-known interest rate
derivatives embedded in callable debt instruments or traded in the OTC market
for hedging or speculation purposes, and we adopt an original pricing approach
based on Supervised Learning (SL) algorithms. In particular, we link the price
of a Bermudan Swaption to its natural hedges, i.e. the underlying European
Swaptions, and other sound financial quantities through SL non-parametric
regressions. We test different algorithms, from linear models to decision
tree-based models and Artificial Neural Networks (ANN), analyzing their
predictive performances. All the SL algorithms result to be reliable and fast,
allowing to overcome the computational bottleneck of standard Monte Carlo
simulations; the best performing algorithms for our problem result to be Ridge,
ANN and Gradient Boosted Regression Tree. Moreover, using feature importance
techniques, we are able to rank the most important driving factors of a
Bermudan Swaption price, confirming that the value of the maximum underlying
European Swaption is the prevailing feature.
