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Modelling of financial risk using forward-looking distributions derived from contingent claims
Thesis (PhD (Actuarial Science))--University of Pretoria, 2022.
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University of Pretoria
2022
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| _version_ | 1867613517416235008 |
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| access_status_str | Open Access |
| author2 | Mare, Eben |
| author_browse | Mare, Eben |
| author_facet | Mare, Eben |
| collection | Thesis |
| dc_rights_str_mv | © 2022 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 | Thesis (PhD (Actuarial Science))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/85201 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:24.530Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| 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:2263/85201 Modelling of financial risk using forward-looking distributions derived from contingent claims Mare, Eben vvanappel@gmail.com Van Appel, Vaughan Density forecasting Recovery theorem Risk management Real-world probabilities Safe retirement withdrawal rates UCTD Natural and agricultural sciences theses SDG-08 Natural and agricultural sciences theses SDG-09 Natural and agricultural sciences theses SDG-10 Natural and agricultural sciences theses SDG-17 Thesis (PhD (Actuarial Science))--University of Pretoria, 2022. In this thesis, we investigate several methods for extracting the forecast distribution from historical asset returns and market-quoted option prices. Typically, risk-neutral distributions, extracted from market quoted option prices, are considered biased estimates of the forecast distribution, and therefore need to be transformed into a real-world distribution. Transformation processes often require the use of historical data and restrictive assumptions on a representative investor. Alternatively, the recovery theorem provides a theoretically appealing method to recover the real-world distribution from the risk-neutral transition probability matrix without the use of historical returns. However, estimating the risk-neutral transition probability matrix has proven to be a challenging task, as it involves solving an ill-posed problem. Therefore, we propose a regularised multivariate Markov chain in the estimation of the risk-neutral transition probability matrix to obtain a more accurate real-world forecast distribution than obtained using the univariate model. Comparative studies on the accuracy of real-world forecast distributions are scarce in the literature. Therefore, we further backtested and compared the accuracy of the extracted distributions on the South African Top 40 index, where we found that the forward-looking real-world distribution improved forecasting in certain situations. We also proposed a forward-looking mixture model of historical and option-implied distributions to improve forecasting. Furthermore, we implemented the extracted forecast distributions in determining safe retirement withdrawal rates. In our empirical study, we showed that the use of forward-looking distributions drastically improved the success in retirement withdrawal rates. bs2025 Actuarial Science PhD (Actuarial Science) Unrestricted SDG-08: Decent work and economic growth SDG-09: Industry, innovation and infrastructure SDG-10: Reduced inequalities SDG-17: Partnerships for the goals 2022-05-16T06:39:22Z 2022-05-16T06:39:22Z 2022 2022 Thesis Van Appel, V 2022, Modelling of financial risk using forward-looking distributions derived from contingent claims, PhD thesis, University of Pretoria, Pretoria, S2022 https://repository.up.ac.za/handle/2263/85201 © 2022 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 | Density forecasting Recovery theorem Risk management Real-world probabilities Safe retirement withdrawal rates UCTD Natural and agricultural sciences theses SDG-08 Natural and agricultural sciences theses SDG-09 Natural and agricultural sciences theses SDG-10 Natural and agricultural sciences theses SDG-17 Modelling of financial risk using forward-looking distributions derived from contingent claims |
| title | Modelling of financial risk using forward-looking distributions derived from contingent claims |
| title_full | Modelling of financial risk using forward-looking distributions derived from contingent claims |
| title_fullStr | Modelling of financial risk using forward-looking distributions derived from contingent claims |
| title_full_unstemmed | Modelling of financial risk using forward-looking distributions derived from contingent claims |
| title_short | Modelling of financial risk using forward-looking distributions derived from contingent claims |
| title_sort | modelling of financial risk using forward looking distributions derived from contingent claims |
| topic | Density forecasting Recovery theorem Risk management Real-world probabilities Safe retirement withdrawal rates UCTD Natural and agricultural sciences theses SDG-08 Natural and agricultural sciences theses SDG-09 Natural and agricultural sciences theses SDG-10 Natural and agricultural sciences theses SDG-17 |
| url | https://repository.up.ac.za/handle/2263/85201 |