Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Modifying copulas for improved dependence modelling
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2021
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613582991032320 |
|---|---|
| access_status_str | Open Access |
| author2 | De Waal, Alta |
| author_browse | De Waal, Alta |
| author_facet | De Waal, Alta |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/78591 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:26.847Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/78591 Modifying copulas for improved dependence modelling De Waal, Alta colette.leroux@porcupine.ai Le Roux, Colette UCTD Statistics, Copulas Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020. Copulas allow a joint probability distribution to be decomposed such that the marginals inform us about how the data were generated, separately from the copula which fully captures the dependency structure between the variables. This is particularly useful when working with random variables which are both non-normal and possibly non-linearly correlated. However, when in addition, the dependence between these variables change in accordance with some underlying covariate, the model becomes significantly more complex. This research proposes using a Gaussian process conditional copula for this dependence modelling, focusing on time as the underlying covariate. Utilising a Bayesian non-parametric framework allows the simplifying assumptions often applied in conditional dependency computation to be relaxed, giving rise to a more flexible model. The importance of improving the accuracy of dependency modelling in applications such as finance, econometrics, insurance and meteorology is self-evident, considering the potential risks involved in erroneous estimation and prediction results. Including the underlying (conditional) variable reduces the chances of spurious dependence modelling. For our application, we include a textbook example on a simulated dataset, an analysis of the modelling performance of the different methods on four currency pairs from foreign exchange time series and lastly we investigate using copulas as a way to quantify the coupling efficiency between the solar wind and magnetosphere for the three known phases of geomagnetic storms. We find that the Student’s t Gaussian process conditional copula outperforms static copulas in terms of log-likelihood, and performs particularly well in capturing lower tail dependence. It further gives additional information about the temporal movement of the coupling between the two main variables, and shows potential for more accurate data imputation. CSIR DSI-Interbursary Support Programme, UP Postgraduate Masters Coursework Bursary Statistics MSc (Advanced Data Analytics) Unrestricted 2021-02-15T09:11:39Z 2021-02-15T09:11:39Z 2021 2020 Mini Dissertation * A2021 http://hdl.handle.net/2263/78591 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Statistics, Copulas Modifying copulas for improved dependence modelling |
| title | Modifying copulas for improved dependence modelling |
| title_full | Modifying copulas for improved dependence modelling |
| title_fullStr | Modifying copulas for improved dependence modelling |
| title_full_unstemmed | Modifying copulas for improved dependence modelling |
| title_short | Modifying copulas for improved dependence modelling |
| title_sort | modifying copulas for improved dependence modelling |
| topic | UCTD Statistics, Copulas |
| url | http://hdl.handle.net/2263/78591 |