Deep Joint Learning valuation of Bermudan Swaptions
<|reference_start|>Deep Joint Learning valuation of Bermudan Swaptions: This paper addresses the problem of pricing involved financial derivatives by means of advanced of deep learning techniques. More precisely, we smartly combine several sophisticated neural network-based concepts like differential machine learning, Monte Carlo simulation-like training samples and joint learning to come up with an efficient numerical solution. The application of the latter development represents a novelty in the context of computational finance. We also propose a novel design of interdependent neural networks to price early-exercise products, in this case, Bermudan swaptions. The improvements in efficiency and accuracy provided by the here proposed approach is widely illustrated throughout a range of numerical experiments. Moreover, this novel methodology can be extended to the pricing of other financial derivatives.<|reference_end|>
@article{casanova2024deep,
    title={Deep Joint Learning valuation of Bermudan Swaptions},
    author={Francisco G'omez Casanova, 'Alvaro Leitao, Fernando de Lope
  Contreras and Carlos V'azquez},
    journal={arXiv preprint arXiv:2404.11257},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11257},
    primaryClass={q-fin.CP cs.NA math.NA}
}