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A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-Dimensional American Options

Author

Listed:
  • Jirong Zhuang

    (University of Macau)

  • Deng Ding

    (University of Macau)

  • Weiguo Lu

    (University of Macau)

  • Xuan Wu

    (University of Macau)

  • Gangnan Yuan

    (Great Bay Institute for Advanced Study
    University of Science and Technology of China)

Abstract

In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR). We incorporate deep kernel learning and sparse variational Gaussian processes to address the challenges traditionally associated with GPR. These challenges include its diminished reliability in high-dimensional scenarios and the excessive computational costs associated with processing extensive numbers of simulated paths. Our findings indicate that the proposed method surpasses the performance of the least squares Monte Carlo method in high-dimensional scenarios, particularly when the underlying assets are modeled by Merton’s jump diffusion model. Moreover, our approach does not exhibit a significant increase in computational time as the number of dimensions grows. Consequently, this method emerges as a potential tool for alleviating the challenges posed by the curse of dimensionality.

Suggested Citation

  • Jirong Zhuang & Deng Ding & Weiguo Lu & Xuan Wu & Gangnan Yuan, 2025. "A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-Dimensional American Options," Computational Economics, Springer;Society for Computational Economics, vol. 66(5), pages 3687-3708, November.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:5:d:10.1007_s10614-024-10833-9
    DOI: 10.1007/s10614-024-10833-9
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    References listed on IDEAS

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    2. Geng, Ru & Zhang, Hong-Kun & Gao, Yixian & Yuan, Gangnan, 2025. "Decoding global economic dynamic: A graph-based examination of contemporary ETF markets," Chaos, Solitons & Fractals, Elsevier, vol. 201(P3).

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