Hedging with Neural Networks
This seminar will take place online.
Johannes Ruf (London School of Economics)
Thursday April 16, 18:00-19:00 (London time)
Register for this meeting
We study the use of 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 given relevant features as input. This network is trained to minimise 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. We illustrate, however, that a similar benefit arises by a simple linear regression model that incorporates the leverage effect. Finally, we argue that outperformance of neural networks previously reported in the literature is most likely due to a lack of data hygiene. In particular, data leakage is sometimes unnecessarily introduced by a faulty training/test data split, possibly along with an additional ‘tagging’ of data. (Joint work with Weiguan Wang)
Johannes Ruf is Professor of Mathematics at the London School of Economics (LSE) and an expert in mathematical finance. Prior to LSE, he was a Senior Research Fellow at the Oxford-Man Institute of Quantitative Finance and a Senior Lecturer at the University College London (UCL). His research interests include machine learning and portfolio theory. Prof. Ruf holds a Ph.D. in Statistics from Columbia University and was a recipient of the “Morgan Stanley Prize for Excellence in Financial Markets”.
This seminar will be broadcast through ZOOM. You need to register to attend this meeting:
The seminar begins at 6:00 PM London time. Participation is limited to 300 participants.
Frontiers in Quantitative Finance is brought to you by the Oxford Mathematical and Computational Finance Group and sponsored by CitiGroup and Mosaic SmartData.