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...
| Váldodahkkit: | , , , |
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| Materiálatiipa: | Conference item |
| Giella: | English |
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Association of Computing Machinery
2022
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| Čoahkkáigeassu: | <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 derivative pricing using higher-order distribution regression. Our methodology leverages the 2nd-order Maximum Mean Discrepancy (MMD), a notion of distance between stochastic processes based on path signatures. To overcome the high computational cost of its calculation, we pre-train a neural network that can quickly and accurately compute higher-order MMDs. This allows the combination of distribution regression with neural networks in a computationally feasible way. We test our model on down-and-in barrier options. We demonstrate that our path-wise approach extends well to the high-dimensional case by applying it to rainbow options and autocallables. Our approach has a significant speed-up over Monte Carlo pricing.</p> |
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