American-type financial instruments are often priced with specific Monte Carlo techniques whose efficiency critically depends on the dimensionality of the problem and the available computational power. Our work proposes a novel approach for pricing Bermudan swaptions, well-known interest rate derivatives, using supervised learning algorithms. In particular, we link the price of a Bermudan swaption to its natural hedges, which include the underlying European swaptions, and other relevant financial quantities through supervised learning non-parametric regressions. We explore several algorithms, ranging from linear models to decision tree-based models and neural networks and compare their predictive performances. Our results indicate that all supervised learning algorithms are reliable and fast, with ridge regressor, neural networks, and gradient-boosted regression trees performing the best for the pricing problem. Furthermore, using feature importance techniques, we identify the most important driving factors of a Bermudan swaption price, confirming that the maximum underlying European swaption value is the dominant feature.
Aiolfi R., Moreni N., Bianchetti M., Scaringi M. (2024). Learning Bermudans. COMPUTATIONAL ECONOMICS, 2024, 1-40 [10.1007/s10614-023-10517-w].
Learning Bermudans
Bianchetti M.Penultimo
;
2024
Abstract
American-type financial instruments are often priced with specific Monte Carlo techniques whose efficiency critically depends on the dimensionality of the problem and the available computational power. Our work proposes a novel approach for pricing Bermudan swaptions, well-known interest rate derivatives, using supervised learning algorithms. In particular, we link the price of a Bermudan swaption to its natural hedges, which include the underlying European swaptions, and other relevant financial quantities through supervised learning non-parametric regressions. We explore several algorithms, ranging from linear models to decision tree-based models and neural networks and compare their predictive performances. Our results indicate that all supervised learning algorithms are reliable and fast, with ridge regressor, neural networks, and gradient-boosted regression trees performing the best for the pricing problem. Furthermore, using feature importance techniques, we identify the most important driving factors of a Bermudan swaption price, confirming that the maximum underlying European swaption value is the dominant feature.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.