Simulations for American Option Pricing Under a Jump-Diffusion Model: Comparison Study between Kernel-Based and Regression-based Methods

Authors:
Hyun-Joo Lee;Seung-Ho Yang;Gyu-Sik Han;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea 790-784;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea 790-784;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea 790-784;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Korea 790-784
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

There is no exact analytic formula for valuing American option even in the diffusion model because of its early exercise feature. Recently, Monte Carlo simulation (MCS) methods are successfully applied to American option pricing, especially under diffusion models. They include regression-based methods and kernel-based methods. In this paper, we conduct a performance comparison study between the kernel-based MCS methods and the regression-based MCS methods under a jump-diffusion model.