Model-agnostic pricing of exotic derivatives using signatures
<p>Neural networks hold out the promise of fast and reliable derivative pricing. Such an approach usually involves the supervised learning task of mapping contract and model parameters to derivative prices.</p> <p>In this work, we introduce a model-agnostic path-wise approach to de...
| Autori principali: | , , , |
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| Natura: | Conference item |
| Lingua: | English |
| Pubblicazione: |
Association of Computing Machinery
2022
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