Welcome to the UPF Digital Repository

Statistical and forecasting techniques on financial markets

Show simple item record

dc.contributor.author He, Yuanxi
dc.date.accessioned 2023-02-14T09:05:39Z
dc.date.available 2023-02-14T09:05:39Z
dc.date.issued 2022-06-30
dc.identifier.uri http://hdl.handle.net/10230/55763
dc.description Treball de Fi de Grau en Economia. Curs 2021-2022
dc.description Tutor: Raúl Merino Fernández
dc.description.abstract In this project, several steps are taken to analyse and cluster the financial markets’ data. The first part is focused on a brief description of the statistical properties of the time series. Later on, the clustering with the k-Means method, using the rolling four statistical moments, is firstly focused on the NASDAQ index, then to extend the model, several financial products are selected. Moreover, the creation of a strategy based on clusters has been proposed in order to get profit, which has been successful. Finally, ARCH models backed by statistical tests and criteria, using standardized returns, are used to capture the volatility clustering and forecast it, and the GARCH (1,1) was selected among all since it has the most forecast accuracy.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.rights © Tots els drets reservats
dc.subject.other Treball de fi de grau – Curs 2021-2022
dc.subject.other Finances – Models matemàtics
dc.title Statistical and forecasting techniques on financial markets
dc.type info:eu-repo/semantics/bachelorThesis
dc.subject.keyword Financial markets
dc.subject.keyword K-Means
dc.subject.keyword Clustering
dc.subject.keyword Forecasting
dc.subject.keyword GARCH
dc.rights.accessRights info:eu-repo/semantics/openAccess

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

In collaboration with Compliant to Partaking