A Deep Learning Approach to Data-Driven Model-Free Pricing and to Martingale Optimal Transport
| Autor: | Ariel Neufeld, Julian Sester |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
FOS: Economics and business
FOS: Computer and information sciences Computer Science - Machine Learning Quantitative Finance - Computational Finance Quantitative Finance - Mathematical Finance Statistics - Machine Learning Computational Finance (q-fin.CP) Machine Learning (stat.ML) Library and Information Sciences Mathematical Finance (q-fin.MF) Machine Learning (cs.LG) Computer Science Applications Information Systems |
| Zdroj: | IEEE Transactions on Information Theory. 69:3172-3189 |
| ISSN: | 1557-9654 0018-9448 |
| DOI: | 10.1109/tit.2022.3229845 |
| Popis: | We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds. In particular, our methodology allows to train a single neural network offline and then to use it online for the fast determination of model-free price bounds of a whole class of financial derivatives with current market data. We show the applicability of this approach and highlight its accuracy in several examples involving real market data. Further, we show how a neural network can be trained to solve martingale optimal transport problems involving fixed marginal distributions instead of financial market data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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