Learning Bermudans

Riccardo Aiolfi (), Nicola Moreni (), Marco Bianchetti () and Marco Scaringi ()
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Riccardo Aiolfi: University of Milan
Nicola Moreni: Intesa Sanpaolo
Marco Bianchetti: University of Bologna
Marco Scaringi: Intesa Sanpaolo

Computational Economics, 2024, vol. 64, issue 5, No 10, 2813-2852

Abstract: 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.

Keywords: Bermudan swaptions; Interest rates; Derivatives pricing; Machine learning; Supervised learning; Correlation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10517-w

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