Gaussian Process Regression for a Single Underlying Autocallable Security

Herbert, Rebecca

Gaussian Process Regression for a Single Underlying Autocallable Security

Thesis / Dissertation

2023

Abstract

Traditionally in Quantitative Finance, in order to price exotic options, particu- larly with path dependency, time consuming Monte Carlo simulations are done. This dissertation considers the use of the machine learning technique Gaussian Process Regression (GPR) as a faster pricing alternative to Monte Carlo simula- tions. The speed of calculation is of interest since prices are linked to fast moving market variables. We focus on the pricing of a single underlying autocallable under GPR against its traditional pricing under the Stochastic Local Volatility (SLV) model. An autocallable is a structured product which allows for early redemption when the underlying meets certain barrier conditions. Due to its path dependency, autocallables are typically priced using Monte Carlo simula- tions under an SLV model which captures realistic market dynamics by allowing volatility to be modelled as stochastic rather than assumed constant, but also allows for more precise calibration by including a local volatility component. We find that a desired level of accuracy is achieved only for autocallable prices under the Schobel-Zhu SLV model, with computation speeds slightly slowler than Monte Carlo simulations.

Keywords

Commerce

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