Workshop: Machine Learning Meets Econometrics (MLECON)

A Bayesian take on option pricing with Gaussian processes

Martin Tegnér · Martin Tegnér


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 market data.