Deep partial hedging
Using techniques from deep learning, we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by Föllmer and Leukert. Not only does this approach better accommodate the realistic setting of hedging i...
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| Veröffentlicht in: | Journal of risk and financial management 2022-01, Vol.15 (5), p.1-5 |
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| container_title | Journal of risk and financial management |
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| creator | Hou, Songyan Krabichler, Thomas Wunsch, Marcus |
| description | Using techniques from deep learning, we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by Föllmer and Leukert. Not only does this approach better accommodate the realistic setting of hedging in discrete time, it also allows for the inclusion of transaction costs as well as general market dynamics. It needs to be noted that, without further modifications, the approach works only if the risk aversion is beyond a certain level. |
| doi_str_mv | 10.3390/jrfm15050223 |
| format | Article |
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| identifier | ISSN: 1911-8074 |
| ispartof | Journal of risk and financial management, 2022-01, Vol.15 (5), p.1-5 |
| issn | 1911-8074 1911-8066 1911-8074 |
| language | eng |
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| source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Bankruptcy Brownian motion Conflicts of interest Costs Hedging machine learning market frictions Neural networks partial hedging Risk aversion risk management transaction costs Volatility |
| title | Deep partial hedging |
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