Neural network libor market model for pricing and hedging interest rate derivatives

In this dissertation, we will introduce a new formulation of variational auto-encoders in order to generate the data we require. Our variational auto-encoder is based on data generation principles from elementary probability i.e. finding the inverse cumulative distribution function and using uniform inputs to generate samples from the distribution. Like all autoencoders, the goal is to reduce the dimensionality in the kernel and use this to describe the data features in the generation. Our formulation will use a kernel which transforms the outputs of the encoder into multi-dimensional uniformly distributed variables, which in turn will learn the cumulative distribution function (in the case of a one dimensional latent space) or the relationship of variables to copula input uniforms (in the case of a multi-dimensional latent space). The decoder will then train to learn the inverse of the encoder and this will then be used to generate data.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2022

Author dc:creator

• Robbertze, Yuri

Advisor dc:contributor.advisor

• Mavuso, Melusi

• Statistical Sciences

Handle dc:identifier.uri

http://hdl.handle.net/11427/36545

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/36545