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.
2024
Aiolfi R., Moreni N., Bianchetti M., Scaringi M. (2024). Learning Bermudans. COMPUTATIONAL ECONOMICS, 2024, 1-40 [10.1007/s10614-023-10517-w].
Aiolfi R.; Moreni N.; Bianchetti M.; Scaringi M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/978335
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