Ruf, Johannes ORCID: 0000-0003-3616-2194 and Wang, Weiguan (2022) Hedging with linear regressions and neural networks. Journal of Business and Economic Statistics, 40 (4). 1442 - 1454. ISSN 0735-0015
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Abstract
We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimize the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. However, a similar benefit arises by simple linear regressions that incorporate the leverage effect.
Item Type: | Article |
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Official URL: | https://www.tandfonline.com/toc/ubes20/current |
Additional Information: | © 2021 The Authors |
Divisions: | Mathematics |
Subjects: | H Social Sciences > HB Economic Theory H Social Sciences > HA Statistics |
Date Deposited: | 09 Dec 2020 11:15 |
Last Modified: | 30 Nov 2024 02:06 |
URI: | http://eprints.lse.ac.uk/id/eprint/107811 |
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