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Pricing high-dimensional American options by kernel ridge regression
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Pricing high-dimensional American options by kernel ridge regression_AAM Jan 2020
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Publication details
| Journal | Quantitative Finance |
|---|---|
| Date | Accepted/In press - 5 Jan 2020 |
| Date | E-pub ahead of print (current) - 19 Feb 2020 |
| Issue number | 5 |
| Volume | 20 |
| Number of pages | 16 |
| Pages (from-to) | 1-16 |
| Early online date | 19/02/20 |
| Original language | English |
Abstract
In this paper, we propose using kernel ridge regression (KRR) to avoid the step of selecting basis functions for regression-based approaches in pricing high-dimensional American options by simulation. Our contribution is threefold. Firstly, we systematically introduce the main idea and theory of KRR and apply
it to American option pricing for the first time. Secondly, we show how to use KRR with the Gaussian kernel in the regression-later method and give the computationally efficient formulas for estimating the continuation values and the Greeks. Thirdly, we propose to accelerate and improve the accuracy of KRR
by performing local regression based on the bundling technique. The numerical test results show that our method is robust and has both higher accuracy and efficiency than the Least Squares Monte Carlo method in pricing high-dimensional American options.
it to American option pricing for the first time. Secondly, we show how to use KRR with the Gaussian kernel in the regression-later method and give the computationally efficient formulas for estimating the continuation values and the Greeks. Thirdly, we propose to accelerate and improve the accuracy of KRR
by performing local regression based on the bundling technique. The numerical test results show that our method is robust and has both higher accuracy and efficiency than the Least Squares Monte Carlo method in pricing high-dimensional American options.
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© 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.
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