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Pricing high-dimensional American options by kernel ridge regression

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  • Wenbin Hu
  • Tomasz Zastawniak

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.

Suggested Citation

  • Wenbin Hu & Tomasz Zastawniak, 2020. "Pricing high-dimensional American options by kernel ridge regression," Quantitative Finance, Taylor & Francis Journals, vol. 20(5), pages 851-865, May.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:5:p:851-865
    DOI: 10.1080/14697688.2020.1713393
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    Cited by:

    1. Jiefei Yang & Guanglian Li, 2023. "On Sparse Grid Interpolation for American Option Pricing with Multiple Underlying Assets," Papers 2309.08287, arXiv.org, revised Sep 2023.

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