Analysis of financial time series using non-parametric Bayesian techniques

Thesis

The overarching aim of this thesis is to show that Gaussian processes and Renyi entropy can be valuable non-parametric tools for forecasting intraday volatility for a wide range of financial time series.

In this thesis empirical volatility forecasting using Gaussian processes (GPs) is presented for stocks, market indices, forex and cryptocurrencies. Key innovations are presented in the application of GPs by using separated negative and positive returns in transformed log space, and...

Expand abstract

Authors: Rizvi, SAA

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: information flow; renyi entropy; machine learning; gaussian processes; financial volatility
• Subjects: renyi entropy; Time-series analysis--Mathematical models; Machine learning; financial volatility; Gaussian processes