Jirong Zhuang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jirong Zhuang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Deng Ding
Department of Mathematics, University of Macau, Taipa, China
2 shared publications - Gangnan Yuan
Great Bay Institute for Advanced Study, Dongguan, China
2 shared publications - Weiguo Lu
Department of Mathematics University of Macau 999078 Macau China
2 shared publications - Xuan Wu
Department of Mathematics University of Macau 999078 Macau China
2 shared publications
Latest Publications
- 3 January 2025
- Published by Springer Nature in Computational Economics
- Vol. 66 (5) , 3687-3708
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-Dimensional American Options
- 3 January 2025
- Published by Springer Nature in Computational Economics
- Vol. 66 (5) , 3687-3708
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-Dimensional American Options
- Open Access
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options
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.
Open AccessA Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options
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.