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...
| Main Authors: | , , , |
|---|---|
| Format: | Conference item |
| Language: | English |
| Published: |
Association of Computing Machinery
2022
|