Modelling of Financial Time Series and Forecasting of their Volatility Using (Bayesian) Regression on Gaussian Processes

Student: Popov Georgy

Educational Programme: Applied Economics (Master)

Year of Graduation: 2020

In our thesis, we compare GARCH-like models and Gaussian Processes regression in the context of modeling and forecasting of financial volatility. The comparison is made through out-of-sample "sliding window" forecasting for 24 financial time series using two different "proxies" for (conditional) daily volatility. In result we find that the forecasting quality of Gaussian Process regression, for given quality metrics, is not inferior to GARCH models.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses