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http://dx.doi.org/10.14477/jhm.2022.35.1.001
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Gaussian Process Regression and Its Application to Mathematical Finance |

Lim, Hyuncheul (Dept. of Math. Chonnam National Univ.) |

Publication Information

Abstract

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.

Keywords

practitioner's Black-Scholes model; Gaussian process regression; Bayesian; volatility; radial basis function;

Citations & Related Records

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