Gaussian Process Regression and Its Application to Mathematical Finance

• 투고 : 2022.01.21
• 심사 : 2022.02.13
• 발행 : 2022.02.28

초록

This paper presents a statistical machine learning method that generates the implied volatility surface under the rareness of the market data. We apply the practitioner's Black-Scholes model and Gaussian process regression method to construct a Bayesian inference system with observed volatilities as a prior information and estimate the posterior distribution of the unobserved volatilities. The variance instead of the volatility is the target of the estimation, and the radial basis function is applied to the mean and kernel function of the Gaussian process regression. We present two types of Gaussian process regression methods and empirically analyze them.

과제정보

Lim supported by NRF-2019R1I1A3A03059382, and BK21 FOUR (Fostering Outstanding Universities for Research, NO.5120200913674) funded by the Ministry of Education(MOE, Korea) and NRF, 이 논문은 전남대학교 학술연구비(과제번호: 2021-2529) 지원에 의하여 연구되었음.

참고문헌

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