A Bayesian take on option pricing with Gaussian processes
Mathematical Finance
2021-12-08 v1 Econometrics
Machine Learning
Abstract
Local volatility is a versatile option pricing model due to its state dependent diffusion coefficient. Calibration is, however, non-trivial as it involves both proposing a hypothesis model of the latent function and a method for fitting it to data. In this paper we present novel Bayesian inference with Gaussian process priors. We obtain a rich representation of the local volatility function with a probabilistic notion of uncertainty attached to the calibrate. We propose an inference algorithm and apply our approach to S&P 500 market data.
Cite
@article{arxiv.2112.03718,
title = {A Bayesian take on option pricing with Gaussian processes},
author = {Martin Tegner and Stephen Roberts},
journal= {arXiv preprint arXiv:2112.03718},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:1901.06021