A Bayesian take on option pricing with Gaussian processes

• Tác giả: Stephen Roberts, Martin Tegner
• Ngôn ngữ: eng
• Ký hiệu phân loại: 372.79 Elementary education
• Thông tin xuất bản: 2021
• Bộ sưu tập: Metadata
• ID: 168326

Tóm tắt

Comment: arXiv admin note: text overlap with arXiv:1901.06021Local 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.